On March 8, International Women's Day (IWD) we celebrate the political, socioeconomic and cultural achievements of women and the women right's movement towards gender equality. "Whilst the social and political rights of women are greater in some places than others, there is no country where gender equality has been achieved" says Mary Evans, professor at the London School of Economics and Political Science in her book "The persistence of gender inequality" (Polity Press 2017). In 2022 this situation has not changed either globally or at the European level as indicated in the EU Gender Equality index for 2020 where the average of the EU is 67.4% and the maximum is Sweden with 83.8%. Although there has been a clear commitment from the European Union on gender equality (specially in innovation and science), there are still structural forms of inequality that must be challenged and changed. It is not the aim of this article to analyse or comment on those, but to show what is being done and is available, especially in the European Union, for us to contribute as individuals and as a community towards gender equality in the field of robotics.

To receive the Vogue Business newsletter, sign up here. For Deloitte, one of the big four accounting firms, the name of its intellectual property protection tool – which counts Jimmy Choo among the global fashion brands using the service – is intentionally bold to match its ambitions. Dupe Killer is a new piece of technology that searches for design infringements using artificial intelligence by learning the shape or configuration of a product and seeking out copies. This is different from detecting counterfeit goods, where the name is stolen and traded upon. Instead, Dupe Killer operates in a world where the only clues are visual.

Since 2010, in the post #Metoo world, the causes and consequences of gender inequalities have come under increasing scrutiny from academics, policy-makers, consumers and the general public. Also during the last decade, concerns about the diffusion of artificial intelligence (AI) have attracted increased attention in the public debate. AI is a "general purpose technology" (GPT), the advances of which create a drop in prediction costs, especially thanks to the "machine learning" domain (Agrawal, Gans & Goldfarb, 2019), meaning the use of data to make predictions. One area that will strongly be impacted by AI is the labor market, a market where gender inequalities have been particularly studied by social scientists. The gender wage gap (the average difference between the wages of men and women) has been deconstructed to investigate the role of attributes (for example differences between men and women in years of education, occupational choices, years of experience…) and the role of discrimination (different effects of the same attributes).

Artificial intelligence is now used in virtually all aspects of our lives. Yet unchecked biases within existing algorithmic systems, especially those used in sensitive use cases like financial services, hiring, policing, and housing, have worsened existing societal biases, resulting in the continued systemic discrimination of historically marginalized groups. As banks increase AI usage in loan and appraisal decisions, these populations are subjected to an even greater precision in denials, eroding protections provided by civil rights laws in housing. Meanwhile, the use of facial recognition technologies among law enforcement has resulted in the wrongful arrests of innocent men and women of color through poor data quality and misidentification. These online biases are intrinsically connected to the historical legacies that predate existing and emerging technologies and stand to challenge the policies created to protect historically disadvantaged populations.

I am regularly asked to summarize my many posts. I thought it would be a good idea to publish on this blog, every Monday, some of the most relevant articles that I have already shared with you on my social networks. Today I will share some of the most relevant articles about Artificial Intelligence and in what form you can find it in today's life. I will also comment on the articles. Artificial Intelligence: The future is data capture, not machine learning.

Rima Seiilova-Olson wasn't sure why she was the only startup founder on a panel full of academics. "I feel a little puzzled," said Seiilova-Olson, co-founder and chief machine-learning scientist at a mental health AI startup Kintsugi, talking to Protocol about her participation in a Feb. 16 federal task force meeting about how she might use a federally funded AI research cloud. The National AI Research Resource, or NAIRR, would be a repository of data and tools for AI research combined with access to the computing power necessary to develop machine learning and other AI systems. But just who will get to use it remains in question. Amid representatives from five colleges and universities, Seiilova-Olson was the lone speaker representing the private sector at the virtual panel discussion addressing the needs of various potential users of the NAIRR.

After an almost year-long buyout process, Microsoft has completed the acquisition of Nuance, folding the latter's voice biometrics and conversational AI capabilities into its portfolio. The $19.7 billion acquisition had to pass several regulatory roadblocks since it was revealed in April 2020. The EU held up the acquisition in December 2021, after receiving permission from U.S. and Australian regulatory bodies prior. Though the EU would eventually give the green light for the deal in the same month. It is the second-largest acquisition by Microsoft, only behind the $27 billion buyout of LinkedIn in 2016.

Social media giant Meta Platforms, formerly known as Facebook, is only the latest company to draw legal heat over its technology -- specifically, its artificial intelligence (AI) innovations. In this episode of "The AI/ML Show" on Motley Fool Live, recorded on Feb. 16, Fool.com contributors Toby Bordelon and Jason Hall discuss how the debate of AI versus privacy continues to rage on. Toby Bordelon: We talked about data protection and privacy, I think, a decent amount with Facebook, and you can see what happens when that goes badly. If you don't follow those rules, $650 million with maybe more to come, and that can put a damper on what you can do. You want data to train AI well.

The objective of this article is to introduce a fairness interpretability framework for measuring and explaining the bias in classification and regression models at the level of a distribution. In our work, we measure the model bias across sub-population distributions in the model output using the Wasserstein metric. To properly quantify the contributions of predictors, we take into account the favorability of both the model and predictors with respect to the non-protected class. The quantification is accomplished by the use of transport theory, which gives rise to the decomposition of the model bias and bias explanations to positive and negative contributions. To gain more insight into the role of favorability and allow for additivity of bias explanations, we adapt techniques from cooperative game theory.

In April 2018, Bronx public defender Kaitlin Jackson was assigned to represent a man accused of stealing a pair of socks from a TJ Maxx store. The man said he couldn't have stolen the socks because at the time the theft occurred, he was at a hospital about three-quarters of a mile away, where his son was born about an hour later. Jackson couldn't understand how police had identified and arrested her client months after the theft. She called the Bronx District Attorney's Office, and a prosecutor told her police had identified her client from a security camera photo using facial recognition. A security guard at the store, the only witness to the theft, later told an investigator from her office that police had sent him a mugshot of her client and asked in a text message "Is this the guy?" Jackson calls that tactic "as suggestive as you can get."