Q&A: Global Challenges Around AI Deployment | MIT News

The AI ​​Policy Forum (AIPF) is an initiative of the MIT Schwarzman College of Computing to move the global conversation about the impact of artificial intelligence from principles to practical policy implementation. AIPF formed at the end of 2020 Brings together leaders in government, business and academia To To develop approaches to address the societal challenges arising out of rapid progress and increasing applicability of AI.

The co-chair of the AI ​​Policy Forum is Alexander Madry, Cadence Design Systems Professor; Asu Ozdaglar, Deputy Dean of Academics at MIT Schwarzman College of Computing and Head of the Departments of Electrical Engineering and Computer Science; and Luis Videgre, senior lecturer at the MIT Sloan School of Management and director of MIT AI Policy for the World Project. Here, they discuss some of the key issues facing the AI ​​policy landscape today and the challenges surrounding AI deployment. The trio are co-organizers of the upcoming AI Policy Forum Summit on September 28, which will further explore the issues discussed here.

Why: Can you just talk about the ongoing work of the AI ​​Policy Forum and the AI ​​policy landscape in general?

Ozdgler: There is no shortage of discussion about AI in various places, but the conversation is often of a high standard, focused on questions of ethics and principles, or on policy problems alone. The approach that AIPF adopts for its work is to target specific questions with actionable policy solutions and engage directly with stakeholders working in these areas. We work “behind the scenes” with small focus groups to address these challenges and aim to bring visibility to some of the possible solutions with players working on them directly through larger gatherings.

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Why: AI affects many areas, which naturally makes us worry about its reliability. Are there any emerging best practices for developing and deploying dependable AI?

Maddy: The most important thing to understand with regard to deploying reliable AI is that AI technology is not a natural, predetermined phenomenon. It is something created by people. The people who are making some of the design decisions.

Thus we need to pursue research that can guide these decisions as well as provide more desirable solutions. But we also need to deliberate and think carefully about the incentives driving these decisions.

Now, these incentives stem largely from business ideas, but not exclusively. That is, we must also recognize that proper laws and regulations, as well as setting considerate industry standards, have a big role to play here as well.

In fact, governments can create regulations that prioritize the value of implementing AI, while being deeply aware of the associated downsides, pitfalls, and impossibilities. The design of such rules will be an ongoing and evolving process as technology continues to improve and change, and we also need to adapt to socio-political realities.

Why: Perhaps one of the fastest growing domains in AI deployment is in the financial sector. From a policy perspective, how should governments, regulators and lawmakers make AI best in finance for consumers?

Videgere: The financial sector is witnessing several trends that present policy challenges at the intersection of AI systems. For one, there is the issue of interpretability. By law (in the US and many other countries), lenders are required to provide an explanation to customers when they take an action that is harmful in any way, such as denial of a loan, to the interest of the customer. However, as financial services rely more on automated systems and machine learning models, banks’ ability to provide that level of imperative explanation to the “black box” of machine learning weakens. So how should the finance industry and its regulators adapt to this advancement in technology? Perhaps we need new standards and expectations as well as tools to meet these legal requirements.

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Meanwhile, economies of scale and data network impacts are leading to the proliferation of AI outsourcing, and more broadly, AI-as-a-service becoming increasingly common in the finance industry. In particular, we are seeing fintech companies provide other financial institutions with tools for underwriting – be it large banks or small, local credit unions. What does this division of the supply chain mean for the industry? Who is accountable for potential problems in AI systems deployed through multiple layers of outsourcing? How can regulators adapt to guarantee their mandates of financial stability, fairness and other societal standards?

Why: Social media is one of the most controversial sectors of the economy, resulting in many social changes and disruptions around the world. What policies or reforms might be needed to ensure that social media is a force for the public good and not a public detriment?

Ozdgler: The role of social media in society is of concern to many, but the nature of these concerns can vary greatly – some people are not doing enough to stop social media, for example, misinformation and extremism, and Others see it as unnecessary silencing of certain perspectives. The lack of an integrated approach on what the problem is, affects the ability to implement any change. All of this additionally ties in with the complexities of the legal framework spanning the First Amendment, Section 230 of the Communications Civilization Act, and business laws in the US.

However, these difficulties in regulating social media do not mean that there is nothing to be done. In fact, regulators have begun to tighten their controls on social media companies, both in the United States and abroad, whether through antitrust procedures or other means. Notably, Ofcom is already introducing new layers of surveillance across platforms in the UK and EU. Additionally, some have proposed taxes on online advertising to address negative externalities caused by the current social media business model. Therefore, policy tools exist, if there is political will and proper guidance to implement them.

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