Achieving the Bayes optimal binary classification rule subject to group fairness constraints is known to be reducible, in some cases, to learning a group-wise thresholding rule over the Bayes regressor. In this paper, we extend this result by proving that, in a broader setting, the Bayes optimal fair learning rule remains a group-wise thresholding rule over the Bayes regressor but with a (possible) randomization at the thresholds. This provides a stronger justification to the post-processing approach in fair classification, in which (1) a predictor is learned first, after which (2) its output is adjusted to remove bias. We show how the post-processing rule in this two-stage approach can be learned quite efficiently by solving an unconstrained optimization problem. The proposed algorithm can be applied to any black-box machine learning model, such as deep neural networks, random forests and support vector machines. In addition, it can accommodate many fairness criteria that have been previously proposed in the literature, such as equalized odds and statistical parity. We prove that the algorithm is Bayes consistent and motivate it, furthermore, via an impossibility result that quantifies the tradeoff between accuracy and fairness across multiple demographic groups. Finally, we conclude by validating the algorithm on the Adult benchmark dataset.

Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts' opinions. She wants to combine their opinions in order to decide on the most effective intervention. We formally define the notion of an effective intervention, and then consider how experts' causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being \emph{compatible}, and show how compatible causal models can be merged. We then use it as the basis for combining experts' causal judgments. We also provide a definition of decomposition for causal models to cater for cases when models are incompatible. We illustrate our approach on a number of real-life examples.

In a change to the regular format, we're asking members to grab a glass of their favourite tipple and join us for an inspiring and informal evening webinar with last year's phenomenal BIMA Conference keynote speaker Nell Watson, Futurist and Technology Philosopher. In a conversation with bestselling author, technologist and and CEO of Vala Health Pete Trainor, she will share her views on the current situation we find ourselves in and what's next. Eleanor'Nell' Watson is a Machine Intelligence researcher who helped to pioneer Deep Machine Vision at her company QuantaCorp, which enables fast and accurate body measurement from just two photos. In sharing her knowledge as AI Faculty at Singularity University and author of Machine Intelligence courseware for O'Reilly Media, she realised the importance of protecting human rights and putting ethics, safety, and the values of human spirit into A.I. Nell serves as Chair & Vice-Chair respectively of the IEEE's ECPAIS Transparency Experts Focus Group, and P7001 Transparency of Autonomous Systems committee on AI Ethics & Safety, engineering credit score-like mechanisms to safeguard algorithmic trust.

Nintendo is taking legal action against hackers who sell software enabling people to play pirated video games. According to court documents obtained by Polygon, two lawsuits were filed last week in the US against alleged hackers in Ohio and Washington. The defendants, Nintendo's lawsuit claims, are associated with a group of anonymous hackers called'Team Xecuter' who provide the pirating products. According to court documents, the products allow people to circumvent'technological protection measures' designed to prevent Nintendo's games from being copied or accessed. Once Nintendo's safeguards are bypassed, players can download a modified operating system and play games that have been pirated.

In previous years the number of new chatbot launches easily outstripped the number of bots that were given new skills. In 2020, as the post-COVID return to work starts, that trend appears to be changing as businesses empower their existing bots with new features. A regular scan of the news headlines in the many years we have been covering chatbots would result in a neat cache of new bot launches to cover. From Asian and Indian banks to western airlines and a growing number of B2B bots, the trend seemed pretty strong. Part of our interest was in following these bots along, to see how many would be retired early on, perhaps due to faulty design or lack of interest.

In recent years, academicians and corporate professionals have requested greater transparency in the inner workings of artificial intelligence (AI) models, and for many good reasons. In a Harvard Business Review post, Andrew Burt, Immuta's chief legal officer, points out that transparency can help mitigate certain problems, such as fairness, discrimination and trust, in a scenario in which, for example, the new Apple's credit card has been accused of sexist loan models, while Amazon scrapped an AI tool to hire after discovering it discriminated against women. At the same time, it is becoming clear that the disclosure of AI information poses its own risks: greater disclosure of information can make AI more vulnerable to attack, while the more information is reported, the more companies can be susceptible to lawsuits or regulatory actions. "Let's call it the AI transparency paradox: while generating more information about AI could bring real benefits, it could also create new risks. To navigate this paradox, organizations will need to think carefully about how they handle AI risks, the information they generate about these risks, and how that information is shared and protected, "says Burt.

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On 19 February 2020, the European Commission published a white paper on the use of artificial intelligence ("AI") in the EU (the "White Paper"). The White Paper forms part of the Commission President, Ursula Von der Leyen's, digital strategy, one of the key pillars of her administration's five year tenure, recognising that the EU has fallen behind the US and China with respect to the strategic deployment of AI. To tackle this problem, the Commission proposes a common EU approach to'speed up the uptake' of AI in the EU, whilst also tackling the human and ethical implications of AI's fast growing use in the EU, including the possible downsides of its use, such as opaque decision making and hidden, embedded gender and racial discrimination. In order to achieve a common EU approach to AI, and to create "trustworthy" AI that can rival developments in the US and China, the Commission proposes the creation of a regulatory framework for AI. Under the regulatory framework, AI applications deemed'high-risk' will be distinguished from'non high-risk' AI applications.

Some biases in AI might be necessary to satisfy critical business requirements, but how do we know if an AI recommendation is biased strictly for business necessities and not other reasons? A company receives 1000 applications for a new position, but whom should it hire? How likely is a criminal to become a repeat offender if they are released from prison early? As artificial intelligence (AI) increasingly enters our lives, it can help answer those questions. But how can we manage the biases that are in the data sets that AI uses? "AI decisions are tailored to the data that is available around us, and there have always been biases in data, with regards to race, gender, nationality, and other protected attributes.

Tracking users' activities on the World Wide Web (WWW) allows researchers to analyze each user's internet behavior as time passes and for the amount of time spent on a particular domain. This analysis can be used in research design, as researchers may access to their participant's behaviors while browsing the web. Web search behavior has been a subject of interest because of its real-world applications in marketing, digital advertisement, and identifying potential threats online. In this paper, we present an image-processing based method to extract domains which are visited by a participant over multiple browsers during a lab session. This method could provide another way to collect users' activities during an online session given that the session recorder collected the data.