AI Ethic & More

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AI programs are made up of algorithms, or a set of rules that help them identify patterns so they can make decisions with little intervention from humans. But algorithms need to be fed data in order to learn those rules — and, sometimes, human prejudices can seep into the platforms.

The ethics of artificial intelligence is the part of the ethics of technology specific to robots and other artificially intelligent beings. It is typically divided into roboethics, a concern with the moral behavior of humans as they design, construct, use and treat artificially intelligent beings, and machine ethics, which is concerned with the moral behavior of artificial moral agents (AMAs).

While machines are theoretically neutral and without prejudice, there have been cases in recent years that show even algorithms can be biased. Some prejudices held in the real world can filter into AI systems.
"Having access to large and diverse data sets helps to train algorithms to maintain the principle of fairness,"
--Antony Cook, Microsoft's associate general counsel for Corporate, External and Legal Affairs for Asia

-- AGENDA --

[18:15] Registration & Social Networking

[18:50] Mr. Michael Ennis Director @ Deloitte
Topics: Cognitive vs. AI and Ethical considerations

[19:15] Co-organizer John Xu Speech for future of DL Dublin Meetups

He will talk about Differences between AI and Cognitive technologies, also business applications of AI and Cognitive technologies and Ethical considerations.

[19:40] Dr. Javier Morales, Data Scientist @ZalandoE
Topics: Reinforcement Learning in Production Systems

Deep Reinforcement Learning in Production Systems Recent advances such as AlphaGo and AlphaStar have made Deep Reinforcement Learning (Deep RL) one of the most exciting applications of AI. When it comes to real life applications, developing and productionising these models requires overcoming a lot of challenges that are often not presented in classical and widely known RL scenarios such as teaching agents to beat leading champions at a particular game. In this talk I will discuss my team's ongoing work using Deep RL to power more relevant search experiences in Zalando, detailing the customer problem we are solving and the challenges we overcame to get this model live.

[20:15] Mr. Abhishek Khanna & Mr. Aonghus Mcgovern @Accenture
Topics: Recent Research In AI Ethic & Bias

How does Bias comes into AI models? Models are not biased but the data itself generated by society carries lot of direct and implicit bias. We are seeing many AI models like facial detection; Amazon Rekognition, criminal justice bias is some of the cases highlighted which shows that models are having bias. Our work was focussed on integrating four major areas:

An overview of our Fairness Framework.
to understand bias types and their evaluation;
to understand how these different bias types are captured by model.
How to get “human in the loop”.

[20:15] Mr. Michael Ennis Director @ Deloitte
Topics: Cognitive vs. AI and Ethical considerations

He will talk about Differences between AI and Cognitive technologies, also business applications of AI and Cognitive technologies and Ethical considerations.

[20:40] Social Networking

[21:00] Event Closing