The company has faced a string of legal challenges. Facebook, YouTube, Google and Twitter have sent cease-and-desist letters to Clearview - to ask them to stop using pictures from the sites. The UK's Information Commissioner's Office even fined the company for failing to inform people it was collecting photos of them.

The switch from a Model-Centric to a Data-Centric mindset is putting emphasis on data and its quality rather than algorithms, bringing forward new challenges. In particular, the sensitive nature of the information in highly regulated scenarios needs to be accounted for. Specific approaches to address the privacy issue have been developed, as Privacy Enhancing Technologies. However, they frequently cause loss of information, putting forward a crucial trade-off among data quality and privacy. A clever way to bypass such a conundrum relies on Synthetic Data: data obtained from a generative process, learning the real data properties. Both Academia and Industry realized the importance of evaluating synthetic data quality: without all-round reliable metrics, the innovative data generation task has no proper objective function to maximize. Despite that, the topic remains under-explored. For this reason, we systematically catalog the important traits of synthetic data quality and privacy, and devise a specific methodology to test them. The result is DAISYnt (aDoption of Artificial Intelligence SYnthesis): a comprehensive suite of advanced tests, which sets a de facto standard for synthetic data evaluation. As a practical use-case, a variety of generative algorithms have been trained on real-world Credit Bureau Data. The best model has been assessed, using DAISYnt on the different synthetic replicas. Further potential uses, among others, entail auditing and fine-tuning of generative models or ensuring high quality of a given synthetic dataset. From a prescriptive viewpoint, eventually, DAISYnt may pave the way to synthetic data adoption in highly regulated domains, ranging from Finance to Healthcare, through Insurance and Education.

At GOAT Group, the Engineering team is an integral part of our dynamic company. By joining the team, your skills will be front and center, working alongside other passionate individuals to solve problems and build software. From launching compelling new consumer experiences, tackling global logistics challenges to scaling infrastructure to facilitate our rapid growth – technology is essential to driving our vision forward. The work you do will change the way the world shops, while also empowering entrepreneurs, including individual sellers, brands and boutiques. The Data Engineering team is responsible for building and maintaining data solutions that deliver value to our internal and external stakeholders.

In the absence of a national data privacy law in the U.S., California has been more active than any other state in efforts to fill the gap on a state level. The state enacted one of the nation's first data privacy laws, the California Privacy Rights Act (Proposition 24) in 2020, and an additional law will take effect in 2023. A new state agency created by the law, the California Privacy Protection Agency, recently issued an invitation for public comment on the many open questions surrounding the law's implementation. Our team of Stanford researchers, graduate students, and undergraduates examined the proposed law and have concluded that data privacy can be a useful tool in regulating AI, but California's new law must be more narrowly tailored to prevent overreach, focus more on AI model transparency, and ensure people's rights to delete their personal information are not usurped by the use of AI. Additionally, we suggest that the regulation's proposed transparency provision requiring companies to explain to consumers the logic underlying their "automated decision making" processes could be more powerful if it instead focused on providing greater transparency about the data used to enable such processes. Finally, we argue that the data embedded in machine-learning models must be explicitly included when considering consumers' rights to delete, know, and correct their data.

Artificial intelligence (AI) researcher Sandra Wachter says that although the House of Lords inquiry into police technology "was a great step in the right direction" and succeeded in highlighting the major concerns around police AI and algorithms, the conflict of interest between criminal justice bodies and their suppliers could still hold back meaningful change. Wachter, who was invited to the inquiry as an expert witness, is an associate professor and senior research fellow at the Oxford Internet Institute who specialises in the law and ethics of AI. Speaking with Computer Weekly, Wachter said she is hopeful that at least some of the recommendations will be taken forward into legislation, but is worried about the impact of AI suppliers' hostility to transparency and openness. "I am worried about it mainly from the perspective of intellectual property and trade secrets," she said. "There is an unwillingness or hesitation in the private sector to be completely open about what is actually going on for various reasons, and I think that might be a barrier to implementing the inquiry's recommendations."

Note: This note is a work-in-progress, created for the course CS 329S: Machine Learning Systems Design (Stanford, 2022). For the fully developed text, see th...

Cities are looking at a number of solutions to these problems. Some require disclosure when an AI model is used in decisions, while others mandate audits of algorithms, track where AI causes harm or seek public input before putting new AI systems in place. What would you like to see cities do to make their use of AI more transparent and fair? It will take time for cities and local bureaucracies to build expertise in these areas and figure out how to craft the best regulations, says Joanna Bryson, a professor of ethics and technology at the Hertie School in Berlin. But such efforts could provide a model for other cities, and even nations that are trying to craft standards of their own, she says.

On Nov. 10, 2021, the New York City Council passed a bill that regulates employers and employment agencies' use of "automated employment decision tools" in making employment decisions. The bill was returned without Mayor Bill de Blasio's signature and lapsed into law on Dec. 11, 2021. The new law takes effect on Jan. 1, 2023. This new law is part of a growing trend towards examining and regulating the use of artificial intelligence (AI) in hiring, promotional and other employment decisions. The new law regulates employers and employment agencies' use of "automated employment decision tools" on candidates and employees residing in New York City.

Privacy watchdogs in Europe are considering a complaint against Apple made by a former employee, Ashley Gjøvik, who alleges the company fired her after she raised a number of concerns, internally and publicly, including over the safety of the workplace. Gjøvik, a former senior engineering program manager at Apple, was fired from the company last September after she had also raised concerns about her employer's approach towards staff privacy, some of which were covered by the Verge in a report in August 2021. At the time, Gjøvik had been placed on administrative leave by Apple after raising concerns about sexism in the workplace, and a hostile and unsafe working environment which it had said it was investigating. She subsequently filed complaints against Apple with the US National Labor Relations Board. Those earlier complaints link to the privacy complaint she's sent to international oversight bodies now because Gjøvik says she wants scrutiny of Apple's privacy practices after it formally told the US government its reasons for firing her -- and "felt comfortable admitting they'd fire employees for protesting invasions of privacy", as she puts it -- accusing Apple of using her concerns over its approach to staff privacy as a pretext to terminate her for reporting wider safety concerns and organizing with other employees about labor concerns. A spokesperson for the ICO told TechCrunch: "We are aware of this matter and we will assess the information provided."

There are movies I enjoyed in childhood that strongly date themselves, presenting visions of a possible future on dates that are now in the past. The ultimate example is Stanley Kubrick's '2001: A Space Odyssey', depicting the arrival of commercial passenger space flight and sentient machines 21 years ago. Other examples include the wonderfully realised dystopian vision of Ridley Scott's'Blade Runner' (set in Los Angeles in November 2019), the flying DeLorean and'Mr. Fusion' generator of'Back to the Future: Part II' (taking us "forward" to the Hill Valley of 2015) and the war with the machines in'The Terminator' and'Terminator 2: Judgement Day' (where Skynet was brought online and triggered a nuclear war in 1997). The technologies shown in these sci-fi touchstones might not have been realised by the advertised dates, but we're getting very close – and a number of developments suggest that 2024 might be the year that reality catches up with these sci-fi futures.