{"id":35380,"date":"2019-11-14T12:09:29","date_gmt":"2019-11-14T12:09:29","guid":{"rendered":"https:\/\/www.intelligentcio.com\/eu\/?p=35380"},"modified":"2019-11-14T12:09:33","modified_gmt":"2019-11-14T12:09:33","slug":"getting-down-to-business-with-data-ethics-in-ai","status":"publish","type":"post","link":"https:\/\/www.intelligentcio.com\/eu\/2019\/11\/14\/getting-down-to-business-with-data-ethics-in-ai\/","title":{"rendered":"Getting down to business with data ethics in AI"},"content":{"rendered":"\n

It seems that now is the time for organisations to be prioritising their ethics efforts to ensure AI is being applied appropriately.<\/em> Joanna Hu, Manager, Data Science (Machine Learning) at Exabeam, explores why CIOs need to make AI ethics a top priority, ensuring their organisation keeps pace with a fast-evolving regulatory landscape.<\/em> <\/p>\n\n\n\n

We are now facing the fourth industrial revolution. Data has become the most valuable asset, due in no small part to the fact that AI technology relies heavily on it. Companies need more access to data than ever before, but with this comes a need to be more open to share data and more importantly, to share data through a secure and well-regulated way without violating privacy. Consumer-based companies also need to standardise and simplify the procedure to allow users to choose whether to share their data or not. <\/p>\n\n\n\n

The UK government\u2019s recent establishment of the world\u2019s first national AI ethics advisory board represents a truly landmark event. Working with government, regulators and industry to lay the foundations for AI, the Centre\u2019s remit is to anticipate gaps in the governance landscape, agree and set out best practice to guide ethical and innovative uses of data, and advise government on the need for specific policy or regulatory action.<\/p>\n\n\n\n

Organisations that develop or use AI-powered systems should take note. According to a recent Deloitte survey, 76% of executives say they expect AI to \u2018substantially transform\u2019 their companies within the next three years. Yet one-third identified ethical risks as one of their top three concerns about the technology.<\/p>\n\n\n\n

The use \u2013 or potential misuse \u2013 of data<\/strong><\/p>\n\n\n\n

AI systems learn from the datasets they are trained with. But how these datasets are compiled can introduce assumptions or biases \u2013 gender, race or income \u2013 that influence how the system behaves. Consider how datasets drawn from a male-dominated field unintentionally resulted in a gender-biased recruitment tool.<\/p>\n\n\n\n

Similarly, a lack of transparency around the data models and\ninformation infrastructures used to power AI systems \u2013 what data is used and\nhow decisions are made \u2013 has the potential to lead to similar issues associated\nwith ethical AI design, development and deployment.<\/p>\n\n\n\n

The fast-paced adoption and reliance on data and Machine Learning in every field of activity \u2013 government, healthcare, agriculture, policing, finance and so forth \u2013 to automate decision-making and transactional tasks, means concerns about the potential misuse of these technologies cannot be ignored.<\/p>\n\n\n\n

The fallout can be seen in the public mistrust of social networking platforms, fuelled by the Cambridge Analytica scandal and growing focus on how organisations use personal information and data. The widespread recognition of the social and public dimensions of AI means concerns that organisations could use AI-powered tools to intentionally \u2013 or inadvertently \u2013 invade privacy or cause harm; unfairness or moral wrongs must be addressed.<\/p>\n\n\n\n

Risk and reward \u2013 The benefits of taking an ethical approach<\/strong><\/p>\n\n\n\n

The UK government\u2019s \u00a39 million investment in a national advisory board is being driven by a desire to ensure safe, ethical and ground-breaking innovation in AI and data-driven technologies \u2013 and propel UK leadership in this arena on the global stage. Earlier this year, a House of Lords\u00a0select committee report on the impact of Artificial Intelligence on the UK\u2019s economy and society\u00a0identified ethics as an area the country could gain a commercial edge on a global scale.\u00a0Indeed, it has been estimated that AI could add an additional \u00a3630 billion to the UK economy by 2035.<\/p>\n\n\n\n

Enterprises and public sector bodies looking to take advantage of these opportunities are recognising that ethical judgements will need to be made about how we use and apply data \u2013 and are putting appropriate accountability structures in place.\u00a0The year 2018 saw a number of major technology vendors \u2013 including Google and IBM \u2013 publish AI ethical guidelines. Meanwhile, the European Commission recently published its Ethics Guidelines for Trustworthy AI<\/em>.<\/p>\n\n\n\n

These moves clearly signal that now is the time for organisations to prioritise their ethics efforts to ensure AI is being applied appropriately \u2013 in other words, that outcomes are fair, transparent, legal and aligned with human values. Future commercial performance and corporate reputations will depend on enforcing appropriate AI standards.\u00a0At a bare minimum, that will require a process of oversight and evaluation, as well as individual accountability.<\/p>\n\n\n\n

The issue of trust and data assets<\/strong><\/p>\n\n\n\n

The UK government is striving to enable sustainable and trusted data infrastructures that maximise data use and value to the benefit of the economy and society in general. It\u2019s a concept that underpins eGovernment, as well as concepts like Smart Cities.<\/p>\n\n\n\n

Working with the Open Data Institute, the government is exploring the potential of \u2018data trusts\u2019 that allow two or more organisations to share data in a safe, fair and ethical way so they can work together to tackle issues at a local level \u2013 enabling open collaboration models that reduce cost and create value.<\/p>\n\n\n\n

When it comes to the development and application of AI, an\nethical framework should take into consideration three key deployment areas:<\/p>\n\n\n\n