Intelligent CIO Europe Issue 25 - Page 79

//////////////////////////////////////////////////////////////////// t TECH cht TALK lk 2018 saw a number of major technology vendors – including Google and IBM – publish AI ethical guidelines. Meanwhile, the European Commission recently published its Ethics Guidelines for Trustworthy AI. These moves clearly signal that now is the time for organisations to prioritise their ethics efforts to ensure AI is being applied appropriately – 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. At a bare minimum, that will require a process of oversight and evaluation, as well as individual accountability. The issue of trust and data assets 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’s a concept that underpins eGovernment, as well as concepts like Smart Cities. Working with the Open Data Institute, the government is exploring the potential of ‘data trusts’ 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 – enabling open collaboration models that reduce cost and create value. When it comes to the development and application of AI, an ethical framework should take into consideration three key deployment areas: • Creation: Does the AI use training data that poses a significant risk to an individual’s right to privacy? Is it representative and does it contain historic biases that could be perpetuated? • Function: Are the assumptions used by AI, and the processes that power these, reasonable and fair? Can anyone understand how AI works and audit how a given output was created? Can you protect against hacking or manipulation? • Outcomes: Is AI being used to do anything unethical? Has appropriate oversight and evaluation been applied? Who is ultimately responsible for decisions made? www.intelligentcio.com The EU’s General Data Protection Regulation (GDPR) represents the first step in a raft of regulations that aim to establish clear governance principles in relation to data. The California Consumer Privacy Act (CCPA) is set to take effect in 2020 and is widely considered to be the most comprehensive privacy legislation in the United States. Organisations must get ahead of this fast- evolving regulatory curve and integrate new control structures and processes designed to manage AI risks and ensure AI technologies are used appropriately. For Luciano Floridi, Chair of The Alan Turing Institute Data Ethics Research Group and Ethics Advisory Board, the success of AI will depend on the use of well-curated, updated and fully reliable datasets. For him, quality and provenance – where the data comes from – is critical. When it comes to addressing legal compliance and ethical issues such as privacy, consent and other social issues, he believes the answer lies in using synthetic data that’s generated by AI itself. In the foreseeable future, he predicts that a move from using anonymised historic data to entirely synthetic data will be key to ensuring privacy or confidentiality is not infringed at the development stage of AI “ ORGANISATIONS NEED TO ACT NOW OR RISK POTENTIAL EXPOSURE TO FINANCIAL LOSS AND REPUTATIONAL DAMAGE IF THEIR APPROACH TO AI IS NOT ETHICAL. Joanna Hu, Manager, Data Science (Machine Learning) at Exabeam solutions – although he acknowledges that predictive privacy harms at the deployment stage will still need to be managed carefully. Action steps Organisations need to act now or risk potential exposure to financial loss and reputational damage if their approach to AI is not ethical. With regulators and governments preparing to address the AI ecosystem, establishing a dedicated AI governance and ethics advisory board that includes cross-functional leaders and advisers should be the first priority. When it comes to the development and deployment of AI technologies, companies will need to be confident these systems do not unintentionally encode bias or treat people – employees, customers, or other parties – unfairly. That means putting tools, processes and control structures in place and ensuring that development teams are appropriately trained on ethical AI practices. Responsibility now falls on the shoulders of data scientists and organisations to act ethically and keep their finger on the pulse when it comes to the evolving regulatory landscape. The ability to take advantage of AI technologies and capture potential future value depends upon it. n INTELLIGENTCIO 79