This week we begin with an article on algorithmic biases in health systems and ways to promote fairness in AI healthcare. Next, we analyse how the use of phrenology in facial recognition technologies can contribute to the stigmatization of marginalized communities. The following article deals with vector databases and how they constitute the infrastructure for machine learning deployment in companies. After that, we assess the long-term impact and economic cost of data breaches on major global brands. Next is an article on the public dispute between Google and Microsoft over the erosion of local journalism. Finally, we have a video on non-fungible tokens (NFTs), how they work, and their boom in the digital art market.
Why biased AI can be damaging to your health
Artificial intelligence holds great promise for improving human health by helping doctors make accurate diagnoses and treatment decisions. It can also lead to discrimination that can harm minorities, women, and economically disadvantaged people. The question is, when health care algorithms discriminate, what recourse do people have?
What facial recognition and the racist pseudoscience of phrenology have in common
‘Phrenology’ has an old-fashioned ring to it. It sounds like it belongs in a history book, filed somewhere between bloodletting and velocipedes. We’d like to think that judging people’s worth based on the size and shape of their skull is a practice that’s well behind us. However, phrenology is once again rearing its lumpy head. In recent years, machine-learning algorithms have promised governments and private companies the power to glean all sorts of information from people’s appearance.
It’s Time to Start Paying Attention to Vector Databases
The concepts underpinning vector databases are decades old, but it is only relatively recently that these are the underlying “secret weapon” of the largest webscale companies that provide services like search and near real-time recommendations. Like all good clandestine competitive tools, the vector databases that support these large companies are all purpose-built in-house, optimized for the types of similarity search operations native to their business (content, physical products, etc.).
Data breach could cost world’s top brands up to $223 billion, finds study
The world’s top brands across sectors might lose between $93 billion and $223 billion because of a data breach, a first-of-its-kind study by Interbrand and Infosys, called ‘Invisible Tech, Real Impact’, has found. This represents 4-9.6 per cent of their cumulative value. The study gains significance in the backdrop of yet another massive hack, this time of Microsoft’s email software, which is estimated to have affected at least 60,000 known victims globally, according to Bloomberg.
Google and Microsoft are in a public feud
Google and Microsoft openly sparred on Friday as the latter prepared to testify at a Congressional hearing focusing on Big Tech’s impact on local news. Microsoft (MSFT) targeted Google’s dominance in advertising as it described in congressional testimony how the tech industry has contributed to the erosion of local journalism.
NFTs Are Fueling a Boom in Digital Art. Here’s How They Work
Non-fungible tokens, or NFTs, have exploded onto the digital art scene this past year. Proponents say they are a way to make digital assets scarce, and therefore more valuable. WSJ explains how they work, and why sceptics question whether they’re built to last.
Source: https://mailchi.mp/zigram/data-asset-weekly-dispatch_15_march_3
