The potential risk of AI systems unintentionally embedding and reproducing bias has attracted the attention of machine learning practitioners and society at large. As policy makers are willing to set the standards of algorithms and AI techniques, the issue on how to refine existing regulation, in order to enforce that decisions made by automated systems are fair and non-discriminatory, is again critical. Meanwhile, researchers have demonstrated that the various existing metrics for fairness are statistically mutually exclusive and the right choice mostly depends on the use case and the definition of fairness. Recognizing that the solutions for implementing fair AI are not purely mathematical but require the commitments of the stakeholders to define the desired nature of fairness, this paper proposes to draft a toolbox which helps practitioners to ensure fair AI practices. Based on the nature of the application and the available training data, but also on legal requirements and ethical, philosophical and cultural dimensions, the toolbox aims to identify the most appropriate fairness objective. This approach attempts to structure the complex landscape of fairness metrics and, therefore, makes the different available options more accessible to non-technical people. In the proven absence of a silver bullet solution for fair AI, this toolbox intends to produce the fairest AI systems possible with respect to their local context.

March is Women's History Month in the US, the UK and Australia, a time to honour women's sometimes underrated contributions to society. According to the US National Women's History Museum, Women's History Month started in 1978 as a local "Women's History Week" celebration in California, with organizers selecting the week to correspond with the March 8 International Women's Day. The US Congress in 1987 passed Public Law 100-9 designating March as the Women's History Month. The past few decades have seen a steady increase in the number of women studying and excelling in the STEM fields. But this is not so in computer science -- the number of women studying or pursuing a career in computer science has been decreasing since around 1990.

If artificial intelligence is truly our fasting moving technology, the law has been lagging far behind. Addressing the emerging legal issues requires an understanding of the technology and how it works. In his Technology Law column, Peter Brown examines how AI functions and some of its legal implications.

Timnit Gebru was wary of being labelled an activist. As a young, black female computer scientist, Gebru – who was born and raised in Addis Ababa, Ethiopia, but now lives in the US – says she'd always been vocal about the lack of women and minorities in the datasets used to train algorithms. She calls them "the undersampled majority", quoting another rising star of the artificial intelligence (AI) world, Joy Buolamwini. But Gebru didn't want her advocacy to affect how she was perceived in her field. "I wanted to be known primarily as a tech researcher. I was very resistant to being pigeonholed as a black woman, doing black woman-y things."

Everyone is talking about artificial intelligence (AI), but few understand how it can be used to improve our everyday access to justice as citizens. In a fun and informative talk, Phillip Djwa will examine the advantages--and limitations--of AI and its application to legal issues people face every day. How far can technology go to help us? Using a chatbot built as a case study, Djwa looks at the state of the industry, issues of built-in bias of computer systems and the general fear we have of being taken over by AI machines like SkyNet. Drew Jackson, head of the People's Law School's chatbot project, will join Djwa.

The war of words between Britain and the EU has begun ahead of next month's trade talks. But as Britain sets its own course on everything from immigration to fishing, there is one area where the battle for influence is only just kicking off: the future regulation of artificial intelligence. As AI becomes a part of our everyday lives -- from facial recognition software to the use of "black-box" algorithms -- the need for regulation has become more apparent. But around the world, there is rigorous disagreement about how to do it. Last Wednesday, the EU set out its approach in a white paper, proposing regulations on AI in line with "European values, ethics and rules". It outlined a tough legal regime, including pre-vetting and human oversight, for high-risk AI applications in sectors such as medicine and a voluntary labelling scheme for the rest.

Micron has acquired FWDNXT, a machine learning software and hardware startup that spun out of Purdue. Micron is integrating FWDNXT's artificial intelligence hardware and software technology with its advanced memory to explore deep learning solutions for data analytics, particularly in IoT and edge computing. Purdue provided the entrepreneurial resources to help me achieve my vision of taking our work on machine learning and deep learning technology to a much wider audience where we can have a bigger impact," said Eugenio Culurciello, Micron fellow and chief machine learning architect. "Micron has the leadership in memory, long history of innovation and drive to deliver power and performance capabilities that address the most complex and demanding edge applications at scale." Culurciello founded FWDNXT while working as an associate professor in Purdue's College of Engineering. Based in the Purdue Research Park, FWDNXT designed next-generation hardware and software for deep learning aimed at enabling computers to understand the world in the same way humans do. Culurciello worked closely with the Purdue Research Foundation Office of Technology Commercialization to secure and develop an intellectual property rights strategy for the AI technology that he developed at Purdue, which Micron licenses today. The FWDNXT acquisition is another strong show of confidence by industry in Purdue technology designed to make a difference for Indiana and beyond," said Brooke Beier, vice president of the Office of Technology Commercialization.

Dr. Truby is Director of the Centre for Law & Development at Qatar University College of Law, alegal research and policy centre focused on delivering solutions to the needs of Qatar's National Development Strategy. Its current research and roundtable agenda focuses upon financial innovation for Qatar's economic diversification, including artificial intelligence, cybersecurity, digital currencies and blockchain technology. As a lawyer and academic established in law, policy and social sciences, he has secured major research grants from Qatar Foundation as well as other corporate and public sponsors, enabling him to research and publish in areas of interest including financial innovation and regulation, cybersecurity, AML/CFT, taxation and commercial law. He also studies policy tools to impact social behavior towards to achieve decarbonization and other sustainability objectives to mitigate climate change. Before joining QU College of Law in 2010, Dr. Truby taught graduate and undergraduate courses on the LLM and LLB courses at Newcastle Law School (England).

Machine learning applications in the legal field are numerous and diverse. In order to make contribution to both the machine learning community and the legal community, we have made efforts to create a model compatible with the classification of text sequences, valuing the interpretability of the results. The purpose of this paper is to classify legal proceedings in three possible status classes, which are (i) archived proceedings, (ii) active proceedings and (iii) suspended proceedings. Our approach is composed by natural language processing, supervised and unsupervised deep learning models and performed remarkably well in the classification task. Furthermore we had some insights regarding the patterns learned by the neural network applying tools to make the results more interpretable.

The European Parliament's proposal to create a new legal status for artificial intelligence (AI) and robots brought into focus the idea of electronic legal personhood. This discussion, however, is hugely controversial. While some scholars argue that the proposed status could contribute to the coherence of the legal system, others say that it is neither beneficial nor desirable. Notwithstanding this prospect, we conducted a survey (N=3315) to understand online users' perceptions of the legal personhood of AI and robots. We observed how the participants assigned responsibility, awareness, and punishment to AI, robots, humans, and various entities that could be held liable under existing doctrines. We also asked whether the participants thought that punishing electronic agents fulfills the same legal and social functions as human punishment. The results suggest that even though people do not assign any mental state to electronic agents and are not willing to grant AI and robots physical independence or assets, which are the prerequisites of criminal or civil liability, they do consider them responsible for their actions and worthy of punishment. The participants also did not think that punishment or liability of these entities would achieve the primary functions of punishment, leading to what we define as the punishment gap. Therefore, before we recognize electronic legal personhood, we must first discuss proper methods of satisfying the general population's demand for punishment.