{"id":1877,"date":"2022-02-23T10:14:16","date_gmt":"2022-02-23T10:14:16","guid":{"rendered":"https:\/\/www.calligo.io\/data-privacy-ai-is-it-even-possible\/"},"modified":"2023-09-11T11:41:24","modified_gmt":"2023-09-11T10:41:24","slug":"data-privacy-ai-is-it-even-possible","status":"publish","type":"post","link":"https:\/\/www.calligo.io\/insights\/data-privacy\/data-privacy-ai-is-it-even-possible\/","title":{"rendered":"DATA PRIVACY & AI: Is it even possible?"},"content":{"rendered":"

Data Privacy and AI: Is it even possible? Now, there\u2019s a loaded question. With so many preceding it: What do we mean by AI? Which aspects of what data are we referring to when it comes to AI? Are ethical practices universal? So, here are a few things to consider.<\/p>\n

<\/p>\n

What data are we talking about?<\/h3>\n

Are we talking about the data going in to form an algorithm? Or the output? Does privacy mean we are protecting people from the data scientists themselves, or external actors? Add into the mix large amounts of data from a multitude of disparate places \u2013 and even data previously thought of as anonymous can end up identifying someone by linking bits of ad hoc information together.<\/p>\n

Transparency, awareness & risk<\/h3>\n

And yet, it\u2019s not just who has access to your data \u2013 it\u2019s about transparency and awareness as to what is happening to it, too. Are you happy for your data to be used in the learning data? Are you aware of why certain decisions have been made? That\u2019s extremely important.<\/p>\n

Then there\u2019s risk appetite. Individuals, societies and businesses all have different risk tolerances. We all know that a company\u2019s risk appetite can be risk averse or more tolerant of it, but the same applies to individuals. Some people would rather pay a subscription online and get a free deal \u2013 but be advertised to. Others would rather pay than have their data shared with other companies and a profile built about them accordingly.<\/p>\n

Cultural considerations<\/h3>\n

There are also cultural nuances at play. We often talk about data privacy<\/a> as though it\u2019s a fixed concept. But notions on this are not the same across the globe. Different societies place different weight on the rights of society versus that of the individual; which has a knock-on effect on what is viewed as acceptable. The COVID vaccine uptake is a good example of when society can come before the needs of individuals, and that\u2019s before you even get to the laws: the GDPR<\/a>, for example, is about governing the use of data, where, for many US laws, it\u2019s more about how you share it.<\/p>\n

Bias \u2013 good or bad?<\/h3>\n

We tend to discuss data bias<\/a> as something inherently bad. And it can be, of course. The Brookings Institution\u2019s Artificial Intelligence and Emerging Technology (AIET) Initiative carried out research in that showed how a platform\u2019s tools allowed advertisers to specifically target \u2018white, African American, Asian or Hispanic users\u2019<\/a>. And this had the power to discriminate against and marginalise certain demographics \u2013 particularly in the areas of housing, employment or credit.<\/a> In this way, \u2018bad\u2019 bias can then creep into the learning data and have a ripple effect.<\/p>\n

But, what if bias means an algorithm moving towards something unexpected, that could teach us something we didn\u2019t know? Data can move in a way that would never occur to us. Medicine illustrates when bias can be a force for good. When it comes to gender, race and ethnicity, certain drugs react in different ways. If we try and take this bias out of the algorithm, the medicine may not be as effective for certain groups. Equally, if attention was paid to gender bias in research on car crashes, fewer women might be seriously injured. Different height, weight, seatbelt usage, and crash intensity would be considered, rather than data from a 50th percentile standard male dummy.<\/a> In some cases, it\u2019s not bias in Machine Learning that needs to go, it\u2019s awareness and monitoring that needs to be ramped up.<\/p>\n

A top-down business issue<\/h3>\n

When it comes to AI and data ethics, we are all still on a journey to determine what good looks like. This topic isn\u2019t just a technical issue or a legal issue – it\u2019s a business issue. A lot of organisations across the world want to be considered ethical. Still more need<\/em> to be perceived that way. But, how can you be considered ethical in general, if you aren\u2019t being ethical with your data? You can\u2019t have it both ways. Data ethics and AI may be a paradox, but that doesn\u2019t mean it isn\u2019t a powerful opportunity for businesses, who can build on foundations of trust – and grow.<\/p>\n

\u00a0<\/h3>\n

\"PICCASO<\/h3>\n

”\u00a0AI and the Ethical Implications of Bias in Machine Learning (ML) Models”<\/h3>\n

Available to watch On-Demand<\/strong><\/a><\/p>\n

Calligo’s VP of Data Ethics and Privacy,\u00a0Sophie Chase-Borthwick<\/a>, will join:
–\u00a0William Malcolm<\/a>\u00a0\u2013 Privacy Legal Director,\u00a0Google
–\u00a0Radha Gohil<\/a>\u00a0\u2013 Data Ethics Strategy Lead,\u00a0Shell
– Anne W. \u2013 Security Specialist,\u00a0Microsoft<\/p>\n

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).<\/p>\n

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.\u00a0<\/p>\n

WATCH ON-DEMAND<\/a><\/p>\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

What do we mean by AI? Which aspects of what data are we referring to when it comes to AI? Are ethical practices universal? 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