Kirby Seminar – Associate Professor Colin Gavaghan

Kirby Seminar Series 2018

Monday, 14 May at 12pm

Lewis Seminar Room, W38 EBL Building

Associate Professor Colin Gavaghan

Associate Professor Colin Gavaghan is the first director of the New Zealand Law Foundation sponsored Centre for Law and Policy in Emerging Technologies. In addition to emerging technologies, Colin lectures and writes on medical and criminal law. Together with colleagues in Computer Science and Philosophy, Colin is the leader of a three-year project exploring the legal, ethical and social implications of artificial intelligence for New Zealand. Colin is a member of the Advisory Committee on Assisted Reproductive Technology and the Advisory Board of the International Neuroethics Network He was an expert witness in the High Court case of Seales v Attorney General, and has advised members of parliament on draft legislation. He dreams of writing science fiction, but compensates with regular appearances on panels at SF conventions.

presents

Thinking Outside the (Black) Box.

The Problems and Perils of Algorithmic Decision-making.
It would be a considerable understatement to say that artificial intelligence (AI) is commanding a great deal of current attention – from media, from national parliaments, even from the European Union. Much of this has focused on the metaphysical intrigues and existential threats of so-called computer ‘superintelligence.’ But the more mundane forms of ‘AI’ that are with us already pose opportunities and challenges of their own. Decision-making algorithms are already in routine use, across government and the private sector. From recommender systems used by Amazon to risk predictions by parole boards, these systems promise greater accuracy and efficiency. But they have also attracted considerable suspicion from those concerned about issues of transparency and bias. In this talk, I will discuss some of these concerns, and some of the regulatory strategies that are being proposed to address them. Is there a way to get the best from the science predictive analytics, while at the same time avoiding the traps of the ‘black box’?

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