Lastly, any use of AI that might lead to a lethal result would require the greatest level of oversight of all DoD's AI systems. This top layer could include systems that are weaponized (even if the weapon itself is not initiated by AI) and systems that may have lethal outcomes (such as cyber tools that may result in lethal effects). One of the goals of a working group overseeing this top layer may be the creation of an oversight body that includes members from outside of the executive branch, including from the legislative branch and from nongovernmental organizations (such as civil liberties advocates and experts from academia). The working group could create policies to dictate how often programs are reviewed by this oversight body, which milestones trigger a review, and so on.

Combining the concepts of individual and group fairness, we search for discrimination by matching individuals in different protected groups to each other, and comparing their classifier outcomes. Specifically, we formulate a GAN-based approximation of the optimal transport mapping, and use it to translate the distribution of one protected group to that of another, returning pairs of in-distribution samples that statistically correspond to one another. We then define the flipset: the set of individuals whose classifier output changes post-translation, which intuitively corresponds to the set of people who were harmed because of their protected group membership. To shed light on why the model treats a given subgroup differently, we introduce the transparency report: a ranking of features that are most associated with the model's behavior on the flipset. We show that this provides a computationally inexpensive way to identify subgroups that are harmed by model discrimination, including in cases where the model satisfies population-level group fairness criteria.

In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.

Who should be on the ethics board of a tech company that's in the business of artificial intelligence (A.I.)? Given the attention to the devastating failure of Google's proposed Advanced Technology External Advisory Council (ATEAC) earlier this year, which was announced and then canceled within a week, it's crucial to get to the bottom of this question. Google, for one, admitted it's "going back to the drawing board." Tech companies are realizing that artificial intelligence changes power dynamics and as providers of A.I. and machine learning systems, they should proactively consider the ethical impacts of their inventions. That's why they're publishing vision documents like "Principles for A.I." when they haven't done anything comparable for previous technologies.

I recently had the pleasure of attending the first AI Tech event to take place in the North of England, something that set a powerful and thought provoking precedent or the future. As part of the Leeds Digital Festival, AI Tech North, was sold out and featured a good mixture of students, programmers and small businesses coming together to hear some of the leading experts in their field share their wealth of knowledge. Anthony Cohn, a Professor in Automated Reasoning at the University of Leeds opened up the event with a lively introduction that gave all in attendance a solid overview of the components of AI, breaking things down into five major categories; perception, language/speech recognition, planning, reasoning (inferring new facts from a basis of existing facts and coming sense) and learning. He stressed that "intelligence can be manifested in different ways" and that "the most successful parts of AI is where there has been little human interaction e.g. The point he made that stuck with me the most however was that the biggest threat to AI and its progress for the foreseeable future is that "the public overestimates the capabilities of AI", something which shows clearly the need for better awareness of exactly what AI is, away from the constraints of science fiction and fantasy.

Many organisations are focused on the "war for talent", with a skills shortage across numerous occupations meaning they face hyper-competition to secure the people they need to sustain and grow their activities. Simultaneously, these organisations are facing near-constant change as they seek to stay competitive and relevant. The emphasis has increased on having employees who embrace change, who withstand the pressures of the modern work environment and create a positive climate for others. Intrinsically linked is the acknowledgement of the benefits that having a diverse workforce can bring – from increased productivity to improved reputation, to the ability to better compete in global markets. In this context, workforce planning – knowing which skills are needed and how they will be engaged – and recruitment become critical activities.

Technological advancement including artificial intelligence (AI) has sparked debate for people and governments in developed countries where democratic systems shape the operation of institutional systems. Specifically, such systems have been driven by established universal values such as respect for human rights, property and privacy rights, and democracy, including freedom of expression, and political participation. In these systems, the advancement of technology has been deployed to enhance the efficiency of governments in providing public services while undergoing public scrutiny and institutional oversight. For example, many cities in developed democratic countries ban the use of facial recognition technology as an instrument of security efforts. However, this may not be the case in developing countries in general and particularly in those undergoing long economic transitions without political liberalization, such as Vietnam and China.

This week, Oxford University announced that American billionaire and philanthropist Stephen A. Schwarzman had gifted the university with its largest cash donation ever--£150 million--to fund (among other things) an institution to investigate the ethics of artificial intelligence. Mr. Schwarzman said that universities need to serve as advisers on the ethics of artificial intelligence and technological advances. While it is certainly true that the technology has moved rapidly ahead of the legislation that patrols it, this is hardly the first time people have thought about the ethics of AI. As any sci-fi buff will tell you, we have been mulling over the ethical ramifications of technologies we didn't possess for a century. What they might not know, however, is that people have been thinking about the potentials and pitfalls of the robot world for thousands of years.

Input distribution monitoring: Recent model input data may be compared with model training data to determine whether incoming credit applications are significantly different from model training data. The more that live data differs from training data, the less accurate the model is likely to be. This data comparison is typically done by looking at variable distributions and ensuring recent data is drawn from a similar distribution as occurred in the model training data. For ML models, multivariate input variable distributions should be monitored to identify input data where combinations of values that were unlikely to appear together during model development are now occurring in production. Systems for monitoring model inputs should trigger alerts to monitors or validators when they spot anomalies or shifts that exceed pre-defined safe bounds.

For an extremely prolonged stretch of time, women working in the fields of science, innovation, engineering and math are doing wonders. Take for instance the tale of Katherine Johnson and her partners, who made noteworthy commitments to the early years of NASA's space program. The world had not in any case known about her name until two years back, when the film, Hidden Figures, hit the screens. Women exceed expectations at communication, sustaining a positive aura in the group, critical thinking, problem-solving among an entire host of other things! Let's have a look at the ladies who are doing everything and motivating us to be a superior version of ourselves each and every day.