Employees of tech companies should have the right to know when they are working on projects they may find ethically unacceptable, a former Google whistleblower has said. In 2018, Jack Poulson hit headlines after he resigned from his job at Google over the company's (now-scrapped) plan to build a censorship AI for the Chinese search market. Now, he wants to make sure that other tech workers can fight for what's right without having to put their livelihood on the line. Poulson has started Tech Inquiry, a non-profit that aims to make it easier for coders with a conscience to speak out inside their companies when they feel ethical boundaries are being crossed. Just as importantly, he's pushing for greater transparency to prevent workers simply being tricked into doing work that they would never take on voluntarily.

Artificial intelligence could one day be used to tailor education to the needs of each individual child.Credit: Suzanne Kreiter/The Boston Globe/Getty People produce more than 2.5 quintillion bytes of data each day. Businesses are harnessing these riches using artificial intelligence (AI) to add trillions of dollars in value to goods and services each year. Amazon dispatches items it anticipates customers will buy to regional hubs before they are purchased. Thanks to the vast extractive might of Google and Facebook, every bakery and bicycle shop is the beneficiary of personalized targeted advertising. But governments have been slow to apply AI to hone their policies and services.

We offer a graphical interpretation of unfairness in a dataset as the presence of an unfair causal path in the causal Bayesian network representing the data-generation mechanism. We use this viewpoint to revisit the recent debate surrounding the COMPAS pretrial risk assessment tool and, more generally, to point out that fairness evaluation on a model requires careful considerations on the patterns of unfairness underlying the training data. We show that causal Bayesian networks provide us with a powerful tool to measure unfairness in a dataset and to design fair models in complex unfairness scenarios.

Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classification system, we characterize the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. We find that these datasets are overwhelmingly composed of lighter-skinned subjects (79.6% for IJB-A and 86.2% for Adience) and introduce a new facial analysis dataset which is balanced by gender and skin type. We evaluate 3 commercial gender classification systems using our dataset and show that darker-skinned females are the most misclassified group (with error rates of up to 34.7%).

Artificial intelligence makes our life easier and more comfortable. But he, rather, uses of them are a threat to our rights and freedoms. Back in the 1970-ies, when even the word "Internet" had not yet been invented, radical philosopher Herbert Marcuse predicted the emergence of certain new technologies, can change the world. On the one hand, they open new prospects for freedom, but will create new forms of exclusion and will give the government and corporations new mechanisms of control over people. But today his prophecy seem to have been carried out. Professor Kameran Ashraf founded the movement Access Now, which works to protect the rights and freedoms of people in the digital age.

Today, as more global manufacturing companies are incorporating artificial intelligence ("AI") into both their manufacturing processes and the products they sell, they are grappling with several pressing issues. At Microsoft, our AI and Ethics in Engineering and Research (AETHER) Committee helped architect six baseline principles for AI development: fairness, reliability & safety, privacy & security, inclusiveness, transparency and accountability to frame the work of the company's AI designers and developers as described in detail in our book The Future Computed: Artificial Intelligence and its role in society. While researching our latest book in the "Future Computed" series, The Future Computed: AI and Manufacturing, we listened to manufacturing company executives, association and work force leaders, as well as front-line factory employees to understand their respective attitudes toward AI-related policies, and the role regulation can play in guiding the use of AI in manufacturing. With respect to the last point, the stakeholders we have connected with and listened to have all stressed that democratizing access to AI is a key priority. They also noted that smart public policy is critical for developing incentives to help all organizations, regardless of size, benefit from AI's potential.

Two City firms have backed a lawtech startup's initiative to create a new standard language of contracting by analysing millions of existing contracts with machine learning software. 'Lexible' is a continually updated dictionary of contract terms which is claimed to have the potential to save huge amounts of lawyers' time by standardising the interpretation and review of contracts. The announcement, by UK startup ThoughtRiver, is signficant because it marks one of the first attempts to exploit the knowledge accumulated by commercial artificial intelligence systems being installed by City firms and in-house legal departments to take on the grunt-work of contract review. ThoughtRiver says its standard language of reference terms is based on an analysis of 4 million documents. This analysis revealed wide variations in phrasing: for example in 1.4 million contracts, a simple governing-law clause was expressed in 330,000 different ways.

Services that Blend Technology and People Are Optimal. Someday in the future complex processes will be able to operate without human intervention. Many complex autonomous processes won't occur in my lifetime. I don't consider warehouse robots nor autonomous vehicles to be terribly complex, it's more a question of repetitions (vehicle miles traveled).

In 2017 Gartner predicted that artificial intelligence (AI) would benefit procurement and sourcing technology. That moment has arrived, according to Mike Quindazzi, managing director at PriceWaterhouseCoopers and top financial-tech influencer. "We're now in the golden age of AI, where advancements come from voluminous sets of data, new algorithms being created, computing power and the ability to do this in the cloud at scale," said Quindazzi. Procurement data has exploded because procurement has evolved into something called "intelligent spend management," which oversees all corporate purchasing processes including direct and indirect purchases, travel and external labor. Quindazzi cautions that while AI has many use cases in procurement, such as rating vendors, "there will always be a human at the end of AI processes, so there needs to be a sense of accountability."

ProPublica provided glaring evidence of this in 2016. A computer program used by U.S. courts wrongly flagged black defendants who did not recidivate over a two-year period as likely to become repeat offenders at nearly twice the rate as white defendants--45 percent as compared to 23 percent. If a human did the same, it would be decried as racist. Our collective experiences are not static. They are shaped by important societal decisions, which in turn are guided by our ethical values.