In recommender systems, the collected data used for training is always subject to selection bias, which poses a great challenge for unbiased learning.

In recommender systems, the collected data used for training is always subject to selection bias, which poses a great challenge for unbiased learning.

We consider the problem of learning from data corrupted by underrepresentation bias, where positive examples are filtered from the data at different, unknown rates for a fixed number of sensitive groups. We show that with a small amount of unbiased data, we can efficiently estimate the group-wise drop-out parameters, even in settings where intersectional group membership makes learning each intersectional rate computationally infeasible. Using this estimate for the group-wise drop-out rate, we construct a re-weighting scheme that allows us to approximate the loss of any hypothesis on the true distribution, even if we only observe the empirical error on a biased sample. Finally, we present an algorithm encapsulating this learning and re-weighting process, and we provide strong PAC-style guarantees that, with high probability, our estimate of the risk of the hypothesis over the true distribution will be arbitrarily close to the true risk.

At the latest Fortune Brainstorm Health virtual discussion on Wednesday, experts from various parts of the medical field said that once these impediments are overcome, A.I. could be the key to improving patient outcomes, lowering overall costs, and reducing burnout and stress on overworked caregivers. One of the first steps, they agreed, is breaking down the barriers that prevent the collection and sharing of accurate, unbiased data. "It's perhaps the most important question of the day: how do we get systems to talk with each other?" said Dr. David Gruen, the chief medical officer of imaging at Merative. "[A.I.] has a broad concept of interoperability. How do we trust the data? How do we get unbiased data? How do we pull together the data that we have in our arms or in the apps on our phones into our health system's record so that we really get a comprehensive picture? We believe that that's going to be a huge hurdle [overcome] when we convince people that this is cost-saving, data-enhancing, and outcome-improving."

Artificial intelligence offers a lot of advantages for organisations by creating better and more efficient organisations, improving customer services with conversational AI and reducing a wide variety of risks in different industries. Although we are only at the start of the AI revolution, we can already see that artificial intelligence will have a profound effect on our lives, both positively and negatively. The financial impact of AI on the global economy is estimated to reach US$15.7 trillion by 2030, with 40% of jobs expected to be lost due to artificial intelligence, and global venture capital investment in AI is growing to greater than US$27 billion in 2018. Such estimates of AI potential relate to a broad understanding of its nature and applicability. AI will eventually consist of entirely novel and unrecognisable forms of intelligence, and we can see the first signals of this in the rapid developments of AI. In 2017, Google's Deepmind developed AlphaGo Zero, an AI agent that learned the abstract strategy board game Go with a far more expansive range of moves than chess.

The technology industry, regulators and privacy advocates continue to debate and push forward the idea that AI development needs to be'responsible and ethical'. However, what that actually looks like - considering so much AI and ML activity is veiled in secrecy - continues to be up for debate. Sure, controls can be put in place, organisations can have strong governance structures, but we are far from an internationally recognised'standard' around how AI should be created and used. This was the topic of debate during a panel at this week's IoT Solutions World Congress in Barcelona, where experts and delegates from industry debated the challenges and pitfalls of developing AI applications and tools ethically. The conversation was one of the more honest ones I've listened to in recent years on the topic. Some conclusions that were drawn included the suggestion that the tech industry should hand over their'black boxes' and trade secrets, and that maybe as a society we should make the decision to just not use some technology.

Artificial intelligence offers a lot of advantages for organisations by creating better and more efficient organisations, improving customer services with conversational AI and reducing a wide variety of risks in different industries. Although we are only at the start of the AI revolution, we can already see that artificial intelligence will have a profound effect on our lives, both positively and negatively. The financial impact of AI on the global economy is estimated to reach US$15.7 trillion by 2030, with 40% of jobs expected to be lost due to artificial intelligence, and global venture capital investment in AI is growing to greater than US$27 billion in 2018. Such estimates of AI potential relate to a broad understanding of its nature and applicability. AI will eventually consist of entirely novel and unrecognisable forms of intelligence, and we can see the first signals of this in the rapid developments of AI. In 2017, Google's Deepmind developed AlphaGo Zero, an AI agent that learned the abstract strategy board game Go with a far more expansive range of moves than chess. Within three days, by playing thousands of games against itself, and without the requirement of large volumes of data (which would normally be required in developing AI), the AI agent beat the original AlphaGo, an algorithm that had beaten 18-time world champion Lee Sedol.

Along with recent notorious data security breaches AI today is probably the most controversial, yet most cherished subject in tech: it is looked upon as either blessing by the likes of Mark Zuckerberg, or being cursed and questioned by Elon Musk and Steven Hawking. Interestingly, those who are raising those concerns, are not really feared about robots taking over and massacring the humanity in'Terminator 2: Judgement Day' best tradition. What the AI-opponents are really worried about lies a bit deeper down: raising awareness of responsible deployment of AI in the era of machine learning algorithms and software becoming mass available. Securing unbiased AI that is not affected by human prejudice about race or gender has long been discussed by researchers. But is there any solid grounding for this fuss in the first place?

Along with recent notorious data security breaches AI today is probably the most controversial, yet most cherished subject in tech: it is looked upon as either blessing by the likes of Mark Zuckerberg, or being cursed and questioned by Elon Musk and Steven Hawking. Interestingly, those who are raising those concerns, are not really feared about robots taking over and massacring the humanity in'Terminator 2: Judgement Day' best tradition. What the AI-opponents are really worried about lies a bit deeper down: raising awareness of responsible deployment of AI in the era of machine learning algorithms and software becoming mass available. Securing unbiased AI that is not affected by human prejudice about race or gender has long been discussed by researchers. But is there any solid grounding for this fuss in the first place?