In this episode of the Beyond Data podcast series, Tessa Jones (Calligo’s Chief Data Scientist) and Peter Matson (ML Solution Architect) are joined by Martin Hoskin, Chief Technologist at VMware and Advisory Board Member for the Centre for Data Ethics & Innovation. In this enlightening discussion, we delve into the concept of data sovereignty and its implications for ethical data use, as well as explore how federated learning offers a promising solution to the challenges we face. 

Understanding Data Sovereignty

Data sovereignty encompasses the notion of data residency, access control, and governance. The dominance of American cloud providers, subject to U.S. laws, raises concerns about data privacy and security, particularly in the European context. For certain organizations, like government agencies and defense suppliers, data sovereignty becomes a critical factor. VMware has introduced a program to certify partners as Sovereign, ensuring data storage, processing, and governance are specified, differentiating them from major hyperscale cloud providers. 

The Challenge of Data Sharing

Data sovereignty also touches upon the ethical dilemma of sharing data for legitimate purposes like law enforcement investigations. Striking a balance between data privacy and the greater good is complex. For instance, the case of Apple’s cloud security raises questions about when governments should access personal data to combat serious crimes. 

Federated learning emerges as a promising solution to data sharing challenges. This approach enables entities to collaboratively train machine learning models without sharing raw data. Instead, local models are trained on separate datasets, and only aggregated model updates are shared with a central server. This preserves privacy and protects sensitive data, making it suitable for applications like fraud detection in the banking industry. 

Experimenting with Federated Learning

The Centre for Data Ethics & Innovation (CDI) conducted an experiment using federated learning for government-provided services. The CDI set up two data sets—one for detecting fraud in financial transactions using SWIFT data and another for studying the spread of COVID-19. The experiment highlighted the complexities of sharing data, including obtaining government buy-in and ensuring data anonymization to protect privacy. 

While federated learning is ingenious, it comes with its own set of challenges. Concerns arise about the aggregator potentially being reverse engineered to extract sensitive information. Additionally, the scale of data involved in real-world applications may make reverse engineering even more difficult. 

As data continues to play a critical role in various industries, addressing data sovereignty and privacy concerns remains paramount. Federated learning offers a way to enable collaboration without compromising data privacy. However, continuous innovation is necessary to tackle challenges like reverse engineering and fully realize the potential benefits of this approach. 

Ethical Considerations in AI and Data Technology

The conversation takes a broader turn, exploring the intersection of AI, data, and ethics. AI development should consider risks, probabilities, and potential biases to build robust and ethical systems. Ethical implications of sharing genetic data and the responsibility of pharmaceutical companies in handling such information are discussed. 

Regulating AI Ethics and the Divide between Academia and Industry

The need for clear regulations to define and enforce ethical standards in AI and data technology is acknowledged. Balancing philosophical academic perspectives with industry practicality becomes essential as AI progresses toward stronger AI with self-learning capabilities. 

Navigating Legal Frameworks and Data Sharing in Healthcare

Enforcing ethical standards and regulations on a global scale, especially with rogue states, poses challenges. Collaboration through global forums, like Gaia X, can facilitate trust, data security, and individual interpretations of frameworks. Standardized data-sharing frameworks and data portability regulations can address data sharing challenges in healthcare. 

Autonomous Weapons and the Role of Global Forums

The ethical challenges of deploying AI in autonomous weapons, especially in making life and death decisions, raise profound moral dilemmas. The hosts stress the importance of engaging in public discourse and involving the global community to shape AI and robotics’ future. 

The Impact of Social Media on Data Privacy

The podcast concludes with a discussion on the influence of social media on data privacy and the ethical considerations surrounding its use. Addressing the impact on young minds and the potential implications on decision-making, including voting rights for 16- and 17-year-olds, is highlighted. 

In conclusion, data sovereignty, AI ethics, and federated learning are crucial components of an evolving data landscape. Ethical considerations must be at the forefront of AI development and data sharing to ensure responsible and equitable data-driven futures. By embracing ethical practices and fostering interdisciplinary collaboration, we can harness the potential of AI while respecting individual rights and privacy. Establishing global forums and transparent public discussions will play a pivotal role in shaping the future of AI and robotics in a manner that benefits humanity as a whole. 

Listen on Spotify or watch below

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ESG – good for the planet, good for business

In January 2023, the EU’s Corporate Sustainability Reporting Directive came into effect. Under its terms, all large companies and all listed companies (except micro-enterprises) must disclose information on the risks and opportunities arising from social and environmental issues, and their impact on people and the environment.

Set against this we have an AI revolution taking place – witness the activity on LinkedIn, with almost every other post lauding the benefits of some ChatGPT derivative or similar, leading to something of an AI feeding frenzy.

Looking through an ESG lens, the environmental impact of AI is huge. According to calculations by the specialist in sustainable data science, Kasper Groes Albin Ludvigsen, published in Medium at the end of 2022, ChatGPT could have consumed as much electricity as 175,000 people in the month of January 2023 alone. Equally, there are numerous articles that reference AI’s huge water impact.

One thing is clear. Whilst there can be many positive outcomes and by products from AI on ESG, the true end-to-end cost of this next wave of Digital Transformation is not yet well understood.

Given we are still trying to get to grips with the effects of the Industrial Revolution from an environmental perspective, how good is humankind’s track record of not repeating the mistakes of the past? How can we exploit opportunity without understanding the true cost and impact?

Wider business benefits of ESG

Developing an ESG strategy that is in harmony with your Digital Transformation yields multiple advantages. And whilst ESG reporting is now mandatory for corporations in the EU, doing so helps quantify the benefits that exist for every party:

• Investors. Many investors place great importance on ESG reporting and an overall strategy
• Customers. Consumers are increasingly concerned about the companies they place business with, and ESG is becoming far more important in their decision making
• Suppliers / Supply Chain. Companies are receiving more requests for information on their ESG credentials, capabilities and response. They must be able to demonstrate their end-to-end position when reporting, driving positive change throughout the supply chain
• Employees. Recruiting and retaining talent can be difficult, expensive and disruptive when there are issues with ESG policies. Research indicates that as many as 47% of employees would look for new roles if their organization is not proactive here
• Market reputation. Creating a strong reputation and a positive view of a company takes time and effort. Negative disclosures around ESG will quickly damage reputations, whereas positive ones will confer competitive advantage

Balancing potential conflicts between digital transformation and ESG

Detractors of ESG will point to the irony that a robust ESG process itself has an environmental impact: data centers in the EU consume more than 2.7% of the bloc’s electricity. And the Ukraine war has highlighted that the geopolitics of power supply will increasingly affect decisions on data processes and sovereignty – when Cloud storage and transference requires so many terawatts of electricity, securing a good price must be balanced against political and geographic risk.

Digital transformation is, by its very definition, a process of huge change. Done right it unlocks competitive advantage, delivers cost savings, drives productivity, opens up new opportunities and delivers compliance with ESG obligations. But done half-heartedly or implemented sporadically it will almost certainly be a huge waste of time, effort and resources.

Deloitte calculates that digital transformation could unlock as much as US$1.25 trillion in additional market capitalization across all Fortune 500 companies. However, done incorrectly, market value could actually be eroded, putting more than US$1.5 trillion at risk.

Prior preparation prevents poor performance

When it comes down to it, successful digital transformation requires only three things:

• An agreed plan
• The right tech platforms
• A joined-up approach
  

And whilst that sounds simple, it involves significant planning and project management resources. It’s not possible to retro-fix a digital solution onto your existing processes – a successful digital transformation requires a center-out approach, incorporating data privacy and protection and considering ESG objectives at the very heart of policy and technology.

When digital transformation is done correctly, “it’s like a caterpillar turning into a butterfly,” but when done wrong, “all you have is a really fast caterpillar.”

MIT Sloan Professor George Westerman

ESG at the heart of the digital transformation process

The comprehensive and insightful data analysis and management required to power your digital transformation needs a huge team of business experts, platform designers and technology specialists, all following a clear process:

• Develop an agreed, business-wide strategy
• Create and share a roadmap
• Define the metrics of success, and measure them
• Build user-friendly dashboards and data analytics
• Use optimal data platforms and cloud services
• Ensure data privacy and protection
• Set and track ESG targets. Not only does ESG need to be considered, it needs to sit right at the heart of digital transformation, informing and guiding the entire organization

Simply ‘ESG washing’ operations with fancy reports is both ineffective and expensive. That’s why Calligo ensures that every digital transformation we drive is engineered with careful attention to its environmental impact. Future-proofing your data use in a way that protects everyone’s future.

To help you navigate the expansive topic of digital transformation, we’ve put together a comprehensive eBook, outlining all the key considerations for your organization. And if all this sounds daunting, don’t worry –  we’ve seen plenty of similar challenges. Data privacy, for example. Once seen as a vague afterthought or something for someone else, today it takes center stage – the concept of Privacy by Design even has its own ISO standard (31700). Understanding the end-to-end ESG impact of Digital Transformation is heading the same way.

If you want to learn some more, or if you want specific advice, consultancy support or technical implementation, why not talk to our experts, who can get your digital transformation journey underway?

The post Powering up ESG through digital transformation appeared first on Calligo.

Exorcizing the ghost in the machine

In this latest podcast in our ‘Beyond Data’ series, Tessa Jones (Calligo’s Chief Data Scientist) and Peter Matson (Data Science Practice Lead) talk with Oxford University’s Professor Philip Howard about the threats posed to democracy by technology, specifically in the shape of Lie Machines.

Fact or fiction? Microtargeting with lie machines

In this age of social media, chatbots and AI it’s never been easier for individuals to share their opinions.  Instant communication to, and engagement with, a global audience is now commonplace, and it seems there’s no need to let facts get in the way of a good angle. As Mark Twain, or maybe Winston Churchill, or most probably Jonathan Swift famously said, “a lie can travel halfway around the world whilst the truth is still putting on its shoes.” A great example in itself of the ease in which misunderstandings and misappropriations can become canon.

In this vein, Professor Howard has spent years studying the mechanisms in which opinion, behavior and values can be manipulated and misdirected by lie machines:

“Lie machines are large, complex mechanisms made up of people, organizations, and social media algorithms that generate theories to fit a few facts, while leaving you with a crazy

conclusion easily undermined by accurate information. By manipulating data and algorithms in the service of a political agenda, the best lie machines generate false explanations that

seem to fit the facts.”

Lie Machines: How to Save Democracy from Troll Armies, Deceitful Robots, Junk News Operations, and Political Operatives

We find lie machines in all types of countries and governing structures. They share common elements – political actors produce the lies, social media firms distribute them, and paid consultants market them. High profile examples of the effectiveness of the lie machine include the UK’s Brexit campaign, and Trump’s electioneering – in both cases patently untrue ‘facts’ and arguments were targeted at key voters by disinformation networks, troll farms and lie machines. Algorithms direct individuals towards ever-more insular sources and extreme content:

 “A healthy, public-facing algorithm might occasionally introduce another credible source…  we know the platforms play around with this stuff, especially during elections in the US”

Controlled by bad actors and forming a global ecosystem of lie development and propagation, these lie machines spread their tendrils across every social media platform, moving out from Facebook as new outlets develop.

Computational propaganda

Lie machines have evolved and finessed themselves as technology advances. Instead of stealing the photos, social media handles and biographies of real people, AI now generates new pictures and personas and thus evades technology platforms’ troll-spotting software.

Spreading propaganda far and wide, with a convincing voice, the lie machine

• Has a profound effect on society, with a scale that is difficult to quantify
• Is perfectly engineered to target human vulnerabilities, reducing critical thinking
• Deliberately misrepresents and appeals to emotions and prejudices, using our cognitive biases to bypass rational thought and create echo chambers
• Is vague and unknowable – what training data was used for large language models? (Professor Howard postulates that every Gmail sent over the last 25 years may have been scraped, along with content from junk news sites)

Doing better – where does the onus sit? User or developer?

When it comes to developing processes to combat the lie machine, there’s no one legislation or guiding principle that works. We must always consider the regional and cultural context of both data and users. Research can’t necessarily be amalgamated or directly compared from different regions and countries – for example, we know that the placebo effect is always greater in US medical studies. To date, technology has not always built in cultural nuances in how people use words, with intent and meaning lost in translation – the majority of network takedown orders are for sites that are not in English.

Wherever there is human input, there are behavioral differences that make it much more difficult to apply common rules:

“People who manage cookies are above average in terms of their knowledge of technology, so these people are generally more purposeful in terms of how they set up their news feeds and where they go for information”

The huge amount of disinformation spread around Covid and the resulting vaccination campaign demonstrates how potent the lie machine is. It doesn’t need to convince people its argument is right, all that is required is to introduce enough doubt, to highlight there is a chance of harm. After all:

“If everybody really understood probability, nobody would ever buy a lottery ticket”

Balance the field – breaking the lie machines

Professor Howard believes that whilst we are justified in our concern about the threats to democracy, the principles behind the lie machine can be harnessed for good – promoting topics that are in the public interest and generating democratic discourse:

“I am cynical, but not fatalistic”

He describes the steps we can take to break the lie machines:

• Public policy oversight, founded in ongoing public data capture and analysis
• Designing social media to highlight emerging consensus, rather than heated conflict – machine learning can amplify common ground
• Setting election guidelines to create more opportunities for civic expression
• Giving journalists, civic groups and researchers access to all the public opinion data that is currently in the hands of the technology firms
• Ensuring that the big data collected by technology platforms is added to public archives

The answer is more social media, not less. But it needs to serve society much better.

IPIE – bringing down the lie machine

Professor Howard has recently launched a new program, creating an independent scientific body to foster global cooperation in safeguarding the online information environment. The International Panel for the Information Environment (IPIE) will assess the scope of the misinformation crisis, analyze its effects on our societies and the planet itself, and propose solutions. Featuring data scientists and engineers alongside neuroscientists and sociologists, IPIE hopes to be the beginning of a global effort to save our common information environment.

Watch the podcast for yourself below to hear more from Professor Philip Howard about the power of the lie machine, and crucially, to learn how we can use it for the collective good.

Professor Philip Howard is a social scientist with expertise in technology, public policy and international affairs. He is Director of Oxford University’s Programme on Democracy and Technology, a Statutory Professor at Balliol College, and he is affiliated with the Departments of Politics and Sociology. Currently, he is also a Visiting Fellow at the Carr Center for Human Rights at Harvard University’s Kennedy School.

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In our latest Beyond Data podcast, co-hosts Sophie Chase Borthwick (our Data Ethics & Governance Lead) and Tessa Jones (our Chief Data Scientist) invited Tomer Elias, Director of Product Management at BigID, to discuss how AI bias affects the LGBTQ+ community.

Here we explore some of the episode’s highlights – although you can also watch the full episode here.

Why is there bias?

When building an AI algorithm or AI solution, it is crucial to make sure it’s based on data sets that are both unbiased and diverse and, in terms of the LGBTQ+ community, this often falls short. Whatever the sector – work, health, entertainment – all will be subject to bias if the LGBTQ+ community is not taken into consideration when an AI solution is being created.

For Tessa Jones, one of the barriers to collecting sufficient data is that people might be reluctant to share information about their sexual orientation or their gender journey – particularly if they don’t know how this personal data will be used. Sophie Chase-Borthwick agrees that it quickly becomes a catch-22 situation:

“The biases that make you nervous of disclosing information are the very reason that you need to disclose said personal information in order to prevent bias and improve.

Knock-on effects

Drawing on his experience as a board member of an organization that supports LGBTQ+ employees, Tomer Elias explains how candidates are being let down by recruitment AI solutions and that the consequences are significant.

“A lot of people in the LGBTQ+ community are unemployed and that’s not because they’re lacking the professionalism and passion.”

Meanwhile, medical advances in the LGBTQ+ community are constantly evolving, and many algorithms do not take these changes into account.

“People who are transitioning are not getting the right treatments because the treatment providers are not well educated about it and the data is not diverse enough,” explains Tomer.

Tessa also raises the issue of health apps that require a user to state whether they are male or female.

“Even though the equations could be written differently to how you use different input, they’re just not and that means, you either have to pretend you’re something different or just not use that tool.”

Potential of AI to help overcome bias

While AI bias is clearly affecting the LGBTQ+ community, there are innovative ways it can be used to overcome it, too. Such as in recruitment.

“At the initial interview stage, AI could be used to scramble the voice so you would not know if the candidate was male or female or someone who has transitioned,” says Tomer.

He also poses the possibility for AI to help with the retention of LGBTQ+ employees.

“Technology could help employers know that the employee is happy and feels a part of the organization.”

Time to step it up… 

There are already many AI forces for good – including recommendation systems which can help LGBTQ+ people feel more emotionally supported and The Trevor Project that uses AI to predict which callers are more likely to commit suicide to ensure they get help.

Much more needs to be done. But the fact that people are starting to think about AI bias and the LGBTQ+ community is a step in the right direction.

“Now we’re talking about it and people are realizing the actual real-world implications, hopefully more people will feel comfortable expressing themselves and we can close some of that data gap so there is more information for the models to work off,” according to our Data Ethics & Governance Lead, Sophie Chase-Borthwick.

“It’s also super critical that we have diverse AI developers who are knowledgeable about people and bias,” adds Calligo’s Tessa Jones.

To hear more of our fascinating discussion on AI bias and how it affects the LGBTQ+ community, tune in to our latest Beyond Data podcast episode below.

The post AI bias is frequently failing the LGBTQ+ community appeared first on Calligo.

Join The Channel Islands’ Premier Data & Cloud Strategy Event – Transform 2022

Our speaker line-up includes Professor Hannah Fry, a Professor in the Mathematics of Cities, science broadcaster, and winner of the prestigious Zeeman Medal.

• Venue: The Royal Yacht Hotel
• Location: Weighbridge Pl, St Helier, Jersey
• Date: 30th November 2022
• Timings: Conference from 1.30 pm-5 pm, cocktails and canapes from 5.30-7 pm

Please Note: This event is for business leaders, and spaces are therefore limited. 

To secure your exclusive place, register here.

Join your peers from across the Channel Islands and get past the buzzwords to learn more about what Business intelligence (BI) really means in today’s modern businesses and why this is a strategic imperative for leadership teams and not just your IT teams.

You will learn about Data and how to unlock its true power, covering:

• The trends in Cloud technology and the business advantages to be gained from them
• Why the Cloud is the foundation to becoming a truly data-driven business
• Best Data practices to help organisations make better decisions
• Using accurate Data to drive change and grasp opportunities quicker
• Eliminate risk and inefficiencies
• Adapt quicker to market challenges

The post The Jersey Transform 2022 Event appeared first on Calligo.

Artificial Intelligence’s ability to augment and support progress and development over the past few decades is inarguable. However, when does it become damaging, contradictory even? In our latest Beyond Data podcast AI’s Climate Jekyll & Hyde – friend and foe, Tessa Jones (our VP of Data Science, Research & Development) and Sophie Chase-Borthwick (our Data Ethics & Governance Lead) discuss exactly this with Joe Baguley, Vice President and Chief Technology Officer, EMEA, VMware.

Our speakers explore the multifaceted topic of energy consumption and AI – from whether all applications are equal for energy consumption (or reflecting if there are some ‘better’ than others), to creating visibility and responsibility of energy consumption for all stakeholders. Here we try to give clarity to some of the grey areas that were discussed.

Should we consider all applications equal?

“AI and machine learning are about huge things, huge data sets, huge computation actions … all of those have huge implications in terms of energy,” Joe observes, before dropping in hugely sobering stats such as the total annual energy consumption of bitcoin being the same as Norway. Even when considering the often-touted argument of 57% of the energy for bitcoin mining using renewables, Joe counters: “But those renewables could have been used for something else, right? Those solar panels… and those hydropower stations and those wind turbines, we could be using them for something else.”

This raises the ethical question of whether there should be stricter governance, standards, and precedent set on more ‘moral’ applications for their energy consumption. Should we be more closely considering the difference in energy consumption between server farms that support minimizing food waste versus those that are focused on mining digital currency, for example?

“Is there an opportunity for [greater] regulation?” Tessa ponders. Would this regulation help challenge the current status quo for all applications’ energy consumption being considered equal? While Sophie observes: “We’ve had certain European nations start to put rules around data center expansion, where you’re allowed and not allowed to build because of the capacity there, which isn’t regulating the use of it. But it does have that knock-on effect that if you literally can’t build the data center support, you have to start thinking about other ways to build [models].”

When considering Sophie’s point on alternative ways to build models, Joe notes: “We’re using AI to deal with the symptoms, but maybe there’s some better ways we could be using AI to deal with the cause as well”.

And this all raises the next question – who should ultimately be making these ongoing moral calls for the environment and energy usage?

Embedding Environmental, Social, and Governance (ESG) by design

Environmental, Social, and Governance (ESG) is shorthand for a framework that helps stakeholders understand how an organization is managing risks and opportunities related to environmental, social, and governance criteria. Our speakers untangle the idea of ESG and how companies could use it to help triage the different applications they use.

Joe asks: “Is there an ESG-led marketing opportunity here? Your AI might be the same as my AI, but my AI is better from an ESG perspective. They both get the same results at the same time for the same cost, but this one’s better from an ESG perspective, in terms of sustainability, in terms of social good, in terms of environmental.”

By placing more emphasis on ESG as the criterion for measuring impact and success, it could help with embedding sustainability in the heart of the application’s deployment, rather than a siloed approach. Sophie agrees: “We have privacy by design, we have security by design. Why not have ESG by design?”

Following on from this thought, our speakers consider the cost implications of AI and ESG with Joe observing, “There’s a lot of businesses right now that can’t afford AI because it’s expensive…but I believe they will come to a tipping point where they can’t afford not to”.

Are we over-prioritizing accuracy?

“There’s a hyper-focus on the accuracy,” according to Tessa. “It ends up not even being about the motivation for green, it’s a motivation for fast training, fast tuning. Unfortunately, it’s how most data scientists are motivated; be faster without having to compromise their accuracy.”

Often, the increase in accuracy can be mapped on a logarithmic graph. Good gains at first, but quickly tapering off to minimal increase. Is it useful to be that much more accurate, often by points of a decimal? “Some are good, more must be better … people just keep going, as opposed to saying actually good enough is good enough,” Joe summarizes.

Instead of chasing marginally better accuracy each time, we should be considering the application in a holistic view. The increase in accuracy might be 0.01%, but would cost heavily for energy consumption – is it worth it? Should we be better at exposing these costs more vigorously throughout a team so everyone can feel more empowered and have the visibility to interrogate more closely?

To hear about how our speakers untangle these controversial questions and more, tune in now to Beyond Data podcast episode 3: AI’s Climate Jekyll & Hyde – friend and foe.

The post The dark side of AI energy consumption – and what to do about it appeared first on Calligo.

In our second Beyond Data podcast episode ‘Autonomous mass transportation and its impact on citizen privacy’, we will sit down with Beep’s Chief Technology Officer, Clayton Tino to explore the current landscape of autonomous vehicles (AVs), whether AVs truly can replace the human factor in public transportation, and how AV ethics can be holistically measured. Here we give you a snapshot of that fascinating discussion by digging into a few of the explored topics.

You can watch episode 1 here

When looking at AV ethics, there are two strands to consider:

1: The ethics programmed into the AV itself (e.g., how the AV ‘decides’ which course to take when it identifies a hazard, otherwise known as the ‘trolley car’ scenario).
2: The ethics surrounding embedding AVs into society (e.g., whether we can truly replace the human factor in AVs, or what level of surveillance AVs should have).

Going beyond the trolley car scenario

Often touted as the litmus test for AV ethics, the ‘trolley car’ or ‘trolley problem’ is a thought experiment where someone chooses between saving five people in danger of being hit by a runaway trolley by diverting the trolley to hit one person. This is extrapolated to AVs by using a scenario such as an AV traveling down the street when suddenly a group of pedestrians runs out. The AV must ‘choose’ between hitting the group or altering its course but by doing so, hitting a lone pedestrian.

The ‘Moral Machine’ experiment was an online survey of 2.3 million people worldwide that investigated the moral dilemmas faced by autonomous vehicles. The study found that moral principles guiding drivers’ decisions varied from country to country, and also women and men viewed ethical and moral situations differently. This made something like the trolley problem difficult to quantify and standardize worldwide.

Far from a simple ethics exercise…

On the surface, it seems a simple ethics exercise. But as Clayton Tino summises: “People like to think they have a preconceived notion of how they would behave, but I just don’t buy that. [A near miss] is a purely reactive response. We’re setting unrealistic expectations on the machine because we need to blame something when something goes wrong.” Tessa Jones (podcast co-host) agrees, observing: “AVs need some decision-making process, but I don’t have a decision making process myself.”

As Sophie Chase-Borthwick (podcast co-host) explains: “We expect our AVs to be guaranteed safe. But we know that any other vehicles are not 100% safe with a human behind them. So we have a higher expectation of what ‘safe’ looks like when it’s autonomous [as opposed to] to when it’s a human.”

In our opinion, the disproportionate emphasis placed on the trolley problem to solve the lion’s share of AV ethics is reductive and dangerous to advancing AV technology. It’s a useful piece of the puzzle but it’s a symptom when we should be focusing on fixing the cause.

In our podcast, we also explore the importance of accurate and timely hazard perception (both in humans and AVs). By improving hazard perception, it not only provides safety methods for AVs but can help reduce or mitigate entirely AVs even having to make the trolley problem decision in the first place. 

Can we ever truly replicate the human factor?

There are five levels in the maturity of autonomy of AVs – with Level 1 being no autonomy and Level 5 being a vehicle without a driver safely taking you to where you want to go.

For Clayton, Tessa and Sophie the debate centers on where the application of AVs could work best with the least blockers. They wonder whether public transportation seems an ideal choice, given how it could be geo-fenced, fixed route and hyper-local.

However, when considering AVs in the context of public transportation, they realize it’s important to look at the holistic service of public transportation, beyond just the driving. As Clayton pithily observes when considering AVs for school buses, “[Bus drivers] do a heck of a lot more than just drive the bus … they need to be aware of passenger safety and security, assistance…”.

For example, in London, there’s been some disputes between wheelchair users and pram users about who has first access to the space. Bus drivers (and others in charge of public transportation) are expected to act as mediators to settle these disputes. How would this be replicated in an AV with no human factor?

The answer could lie in more secure and closely governed surveillance. Having surveillance on public transport AVs could add a safety layer to minimize vandalism, protect the users and ensure the AVs remain a reliable and safe choice. Our podcasters observe the marked differences between privacy in the US and Europe but with the introduction of GDPR-style laws such as the California Consumer Protection Act (CPPA), there will inevitably be more scrutiny on how the surveillance data is used and stored.

However, as is often the case with autonomy when it comes to public transport there’s no easy decision. By removing the human factor, there need to be other allowances made to fill the gap. Companies and governments need to work hard to make sure both the users and their data are protected and that these allowances do not harm the end-users or misuse them for commercial purposes.

Our podcast delves more into the nuances and pitfalls when considering the commoditization of a public service, such as public transportation. Generally, the people who need it most are vulnerable, and unless there’s a significant level of transparency, can users be fully aware and able to consent to the wider implications of being surveilled?

To hear more about how we untangle and much more, watch our episode on ‘Autonomous mass transportation and its impact on citizen privacy ’. 

The post Vehicle Autonomy; the good, the bad, and the complicated appeared first on Calligo.

From the increasing importance of ethical AI principles, to the EU’s all-encompassing data strategy – including the first law on AI by a major regulator, anywhere – and US President Joe Biden’s new transatlantic data agreement, much has been bubbling away in the world since my previous revision of The Data Privacy Periodic Table. 

Here I delve into the whys and wherefores of any changes I’ve made since then – in the form of Update 8.

Download PDF version

Fundamental Principles of Data Protection – some newcomers…

Acting FAST on ethical AI

6-9

The Alan Turing Institute developed the ‘FAST Track Principles’ to support a responsible environment for data innovation, in particular when understanding Artificial Intelligence ethics and safety. To reflect the importance of ‘ethical AI’ (as demonstrated by the ICO’s collaboration with the Institute) I have added Accountability and Sustainability for the first time.

While Sustainability is the only element that’s really unique to AI, Fairness and Transparency (moved, but not new) have and always will be fundamental to data privacy. I had considered Accountability to be almost too obvious and intrinsic a component of privacy to have its own place. But, as a nod to my opinion that the FAST Track Principles should become industry standards, here it is. After all, FST certainly doesn’t have the same ring to it.

While I can’t go into huge detail here about each one, I urge anyone who hasn’t read up on FAST to do so now – and embed the principles into every aspect of AI project delivery. 

“As inert and program-based machinery, AI systems are not morally accountable agents. This has created an ethical breach in the sphere of the applied science of AI that the growing number of frameworks for AI ethics are currently trying to fill. Targeted principles such as fairness, accountability, sustainability, and transparency are meant to ‘fill the gap’ between the new ‘smart agency’ of machines and their fundamental lack of moral responsibility.”

The Alan Turing Institute: Understanding Artificial Intelligence Ethics and Safety

Source: https://www.turing.ac.uk/sites/default/files/2019-08/understanding_artificial_intelligence_ethics_and_safety.pdf

Moved, but not downgraded

34 & 35

Lawfulness and Necessity have made way for FAST. Far from downgraded, they’ve merely moved a little within the same elemental area. But, Relevancy has been removed altogether. In my opinion, this is more than covered by Necessity and there’s no need to double up on similar principles.

Retention becomes the industry norm…

53

We welcome Retention to the table – this echoes the fact that this has become more of an industry standard term.

Highly unstable, yet fascinating Future Developments…

And now for the fast-moving, highly unstable elements: the future developments that are shaping the world’s data privacy parameters and legislation.

US legislation limbo…

112

To the United States and various US Bills – starting with President Joe Biden’s new transatlantic data agreement in principle with the European Union. 

We’ve been here twice before – with similar proposals previously thrown out. Although it doesn’t seem to be going anywhere fast, this is hugely important, due to the rocky recent history of EU-US data flows – following the invalidity of the Safe Harbor and subsequent Privacy Shield framework.

Above all, greater certainty is needed for the vast amount of companies that regularly exchange data between Europe and the US.

Then there’s the ADPPA – the American Data Privacy and Protection Act – a bill designed to regulate how organizations collect, process, manage, and even securely store personal information or “covered data.” The US does not yet have a comprehensive privacy law that creating such safeguards. The ADPPA has bipartisan support, but also faces opposition from privacy advocates and business groups.

After an initial flurry of excitement, how and when these laws will pass is up in the air. In the meantime, individual states are focusing on their own data laws. 

“We have agreed to unprecedented protections for data privacy and security for our citizens. This new arrangement will enhance the Privacy Shield framework, promote growth and innovation in Europe and in the United States and help companies, both small and large, compete in the digital economy.” 

Joe Biden, US President, March 25, 2022

Source: https://www.whitehouse.gov/briefing-room/speeches-remarks/2022/03/25/remarks-by-president-biden-and-european-commission-president-ursula-von-der-leyen-in-joint-press-statement/

Retroactively enforceable California Privacy Rights Act

Staying with US Bills, but moving specifically to California state now, and the CPRA comes into law after January 2023, technically speaking. But – and there’s a big but – companies need to be compliant retroactively. The second the law goes live, businesses can be fined for any non-compliance issues dating back to January 2022. Forewarned is definitely forearmed in this case.

Across the Atlantic…

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To Europe and the EU Data Strategy. Its tagline is: ‘Making the EU a role model for a society empowered by data’. But this is so much more than the EU’s General Data Protection Regulation. It’s about the entire data landscape; a large regulatory umbrella under which the future of Europe’s data protection sits. Having said that, policymakers are far from finished in creating this broader regulation. 

The new laws that will be incorporated into this holistic strategy will include, among others: The Data Act – aiming to create rights and responsibilities on how valuable forms of data are shared; The Data Governance Act – to create a “common European data space” and “single market for data” – boosting innovation while respecting the values of privacy; and the AI Act – the first law on AI by a major regulator, anywhere.

Importantly, none of these acts should be viewed in isolation. It’s a positive development that the EU is treating data as an asset (like physical infrastructure). Sewing all the various initiatives together in this way – data protection, governance, AI and also fair markets – is a savvy, cohesive approach, in my opinion. 

However, it’s hard to know how effective this strategy will be when it comes to improving data development, given the EU currently lags behind on AI / ML. It remains to be seen if this will level the playing field, or create yet more red tape.

“People, businesses and organisations should be empowered to make better decisions based on insights from non-personal data, which should be available to all.”

European Commission 

Source: https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-digital-age/european-data-strategy_en

State of flux

113

In post-Brexit UK, the new UK-GDPR is nearly identical to the EU-GDPR. However, it is UK legislation independent of the EU. The UK has already performed a consultation process to see what data protection in the UK should look like in the future – and therefore new developments need to be monitored closely as they unfold.

114

First it was Apple’s move to block third-party cookies that conduct cross-site tracking on Safari, then Google announced they will do the same in 2023. But, with these changes making things difficult for advertisers and small publishers, what will adtech look like in the future?

Ever-changing laws…

109

Having passed its latest draft of the Personal Data Protection Bill over to the parliament in November 2021, the bill, now referred to as the Data Protection Bill or DPB as it now contains several provisions on non-personal data, has been pulled from consideration for parliament to draft entirely fresh language.

111

The Personal Data Protection Law (PDPL) is the first of its kind to be passed in Saudi Arabia. The protection rules were first published in September 2021 and they are due to come into effect in March 2023.

The Data Privacy Periodic Table is entirely unique to Calligo and is an ongoing project, contributed to by the entire industry. We encourage anyone who’s interested to get involved. I consider all comments when creating the next update.If you have any thoughts you’d like to share or want to discuss anything featured in more detail, you can contact me here.

The post UPDATE 8: The Data Privacy Periodic Table appeared first on Calligo.

Often, data ethics is mistakenly reduced into just being about data bias, but ethics in data is much more. Data ethics is defined as “the values, principles, and techniques people/companies can employ to create standards of the right and wrong development and deployment of AI technologies.” 

There are many negative consequences for unethical data design but the most significant include: 

• Questionable design which could lead to implications such as safety risks.
• Unintended negative consequences, such as individual safety (physical, digital, financial), organizational impact (financial, legal, reputational), or societal (security, stability).
• Misuse of the data.

Establish an ethics-by-design culture

Building off the privacy-by-design approach, ethics-by-design means embedding ethical principles into designing, developing, and delivering your products and services. “We do a Data Protection Impact Assessment (DPIA). I don’t see why we wouldn’t do the same with ethics and ethical assessments” says Sophie. This is especially important for a data strategy service.

Radha agrees: “As AI exponentially grows, we need to deploy learning at scale. AI is growing across teams, not just data scientists. Everyone needs to know the basics so they can interact with AI responsibly.” 

William adds “It’s important to own processes and principles. When building our data ethics approach, we considered what matters to us as a company and reflected this in our frameworks.”

A data ethics framework  can act as a compass point to show true north when you’re mired in data ethics on the ground. Here are important points to consider:

• Build a framework. Work it out. What does this look like on a global scale? 
• Consider what good and ethical looks like for your company. 
• Measure against your framework the same you would for any other system. Measure the input and the output against ethics metrics.

Security is important, but don’t try and boil the ocean 

One of the most important aspects for ethical data is being able to securely store this data. 

According to Sophie, “Security, but from whom? Internal or external actors? Do you use a secure environment which everyone can access but results in privacy issues? We know hackers are reverse engineering algorithms so you should consider who you’re protecting your data from. You could be protecting it from your own data scientists but not the malicious external actor. There’s no one size fits all.”

Anne agrees: “Security can be as complex as you want it to be. Security can also be simple. You should make sure to encrypt data when it’s a function, store it somewhere safe where access is controlled. Use zero trust models and make sure you have visibility across the clouds.”

For Sophie, it’s important “To try not and boil the ocean when it comes to data security. You don’t have to put your whole house in a safe … but you can lock the doors,” she advises. 

In the 21st century, data is the new gold rush. Secure your assets appropriately. 

Embed data ethics in your procurement

Your company might live and breathe data ethics, but what happens if you tender a vendor who doesn’t? This could undo all your hard work. Nick Graham, Partner at Dentons, who was hosting the panel advises: “Dig down deeper than the sales pitch. Uncover how the model actually works.”

How do you maintain ethical practices when liaising with a vendor? Spoiler: exactly the same way you maintain good practice with any other vendor, for example, security. 

As Sophie sums up, “Vendor management isn’t new. You should make sure the vendor has the right checks in place and their data is secure. We know this not just for data ethics but for anything you ever outsource from a supplier.”

If data ethics is the puzzle, data bias is one key piece

Data ethics is the holistic approach, but another crucial aspect of this is data bias. You can read more here in our blog post on how to ‘banish the bias’. 

” AI and the Ethical Implications of Bias in Machine Learning (ML) Models”

Available to watch On-Demand

Data Ethics is a major area for consideration in the world of data, governance, privacy and law. Artificial Intelligence (AI) can perform highly complex problem-solving (such as unravelling intricate cancer diagnoses), but it can also suffer major setbacks (such as the potential for racial discrimination).

AI is outperforming humans at narrowly defined, repetitive tasks, which is the space in which AI excels, there are however some risks associated with AI and during our panel debate, we have invited some leading experts and thought leaders to help us navigate this complex area. 

WATCH ON-DEMAND

The post Create an ethics-by-design approach for data appeared first on Calligo.

Our VP for Data Ethics & Governance, Sophie Chase-Borthwick, recently took part in an expert panel – PICCASO Special Interest Group and joined William Malcolm (Privacy Legal Director at Google), Radha Gohil (Data Ethics Strategy Lead at Shell), and Anne Woodley (Security Specialist at Microsoft) in drilling down into the details of data bias. Here we unpick some of their in-depth discussion.

Understand data bias

Bias in data can take many forms, with bias coming from humans, the data, or even the model itself. Any data governance service, data strategy service or any service which uses data should be aware of bias. Some types are:

• Sample bias (otherwise known as selection bias): using a sample which isn’t reflective of the population, for example training a facial recognition system only on white men. 
• Confirmation bias: the tendency to look for or interpret information that’s consistent with your own beliefs, for example scientists could sometimes selectively analyze and interpret data in a way that confirms their preferred hypothesis.
• Historical bias: when the cultures and societal norms have become mired into systematic processes, for example training a model on historical data which contains gender biases would result in data bias being inherent in the output. 

The impact of bias can be negligible or significant, for example Apple’s racial discrimination in face recognition technology or Amazon’s secret AI recruiting tool that showed bias against women. Bias often impacts the most vulnerable and marginalized. 

Be aware of the ongoing tradeoffs for data

“There are tradeoffs: fairness, accountability, safety,” according to William Malcolm, Privacy Legal Director at Google. “These are key factors in adopting AI solutions but we don’t acknowledge that sometimes they conflict.” 

It’s a key point to consider. Sometimes explainability can conflict with accuracy; you choose simpler algorithms to parse but it impacts the overall output. You could use human intervention to increase the accuracy with manual checks, but then it risks the human bias creeping in. 

While Calligo’s Sophie Chase-Borthwick observes: “Companies want to use algorithms to determine products and use AI to remove biased human beings. But which one is more or less biased?” 

There will always be threats for data bias. You should continually be aware of the ongoing tradeoffs and the implications each one has. 

Mitigating data bias

Now we’ve understood what data bias is, it’s time to consider how you can mitigate these biases. These could take many forms, such as: 

• Checking there’s no infrastructure issues in databases.
• Being mindful when it comes to data processing to identify any possible sources of bias.
• Considering which model is the least biased as well as which model would perform well. 
• Instilling a robust anti-bias culture in your organization, for example training everyone to identify data bias.
• Monitoring real-world performance for your machine learning lifecycle. It’s crucial to never see a model as ‘finished’. There should be continuous monitoring and observing for how well the model is performing. 

There’s no one solution for tackling bias. It’s an ongoing challenge. Throughout the cycle, the biases might keep changing, and so the solution for them must keep changing. 

Humans and machines must work together

In Radha Gohil’s, Data Ethics Strategy Lead at Shell, words,: “We need humans in the loop for verification when we train and govern a model. Humans have an innate ability to identify cultural nuance in a way that an algorithm cannot.” Microsoft’s Security Specialist, Anne Woodley, agrees: “When working with data, the onus is on humans to set up the right checks and balances throughout the cycle so that when bias creeps in, it can be identified quickly.”

This draws on Article 22 for EU GDPR for people having the right to ‘human intervention’ if they want to contest a decision made by an algorithm. This contestation has a legal effect enshrined in EU law. For a data privacy service, this is especially important.

There’s another conundrum to consider. “Ironically, sometimes you can remove some data and it impacts the end of the data. There needs to be a careful balance with the data going in … and the data not going in,” according to Sophie Chase-Borthwick. 

Machines are only as good as the data which is put in, so we should aim to put in the cleanest, most unbiased data possible to get the most actionable and impactful results. 

What’s next for data bias? 

Looking to the future, minimizing data bias will evolve as/when new AI and Machine Learning technologies appear. However, new technologies might create new biases themselves. 

Data bias is just one facet of the wider picture of data ethics. It’s crucial to maintain rigor and avoid complacency when it comes to any aspect of data ethics. 

In our next blog, we’ll be exploring ‘ethics-by-design’. So do stay tuned – or, in the meantime, you can get in touch with our team of experts who can help you with minimizing data bias and ensure ethical data use and insights.  

” AI and the Ethical Implications of Bias in Machine Learning (ML) Models”

Available to watch On-Demand

Data Ethics is a major area for consideration in the world of data, governance, privacy and law. Artificial Intelligence (AI) can perform highly complex problem-solving (such as unravelling intricate cancer diagnoses), but it can also suffer major setbacks (such as the potential for racial discrimination).

AI is outperforming humans at narrowly defined, repetitive tasks, which is the space in which AI excels, there are however some risks associated with AI and during our panel debate, we have invited some leading experts and thought leaders to help us navigate this complex area. 

WATCH ON-DEMAND

The post Banish the data bias: make your data quality and safe appeared first on Calligo.