Technology

Over the weekend, word broke that Univision was planning to sell off Fusion Media, the brand portfolio containing a chunk of The Onion and various media properties purchased in a Gawker fire sale less than two years back. Today, the company confirmed with TechCrunch that it has &initiated a formal process to explore the sale& of both the Gizmodo Media Group and The Onion.

The company isn&t offering much information beyond whatcurrently available in the press release, but the proposed sale includes a bevy of strong media brands, including Gizmodo, Jezebel, Deadspin, Lifehacker, Splinter, The Root, Kotaku, Jalopnik, The Onion, Clickhole, The A.V. Club and The Takeout.

The Spanish language broadcaster purchased the Gizmodo assets from Gawker Media for $135 million back in 2016, after the company was rocked by a Peter Thiel-backed Hulk Hogan lawsuit. The portfolio was rolled up along with Fusion TV into Fusion Media, a millennial-focused pivot into digital by a brand that had traditionally had issues keeping up with the times.

But Univisionyear has seen a CEO shuffle and ongoing restructuring with multiple rounds of layoffs. Univision reportedly attempted to sell a stake in the company last year, but ultimately failed due to &skittish& investors.

This time out, it seems Univision is all in, though it notes that a sale is anything but guaranteed, writing, &There is no assurance that the process to explore the sale of these assets will result in any transaction or the adoption of any other strategic alternative.&

Samsunggot a new smartwatch on the way. That much seems certain. Itbeen about a year since the last big announcement, and the company is about to have two large platforms in the form of AugustNote 9 Unpacked event and BerlinIFA trade show the following month.

A couple of new tidbits, however, are fueling speculation that things might be a little different this time around. First, a trademark filing in Korea for a Samsung Galaxy Watch logo. The company dropped the Galaxy bit from its Gear line between the first and second generation watches, back in 2014.

Among the more notable changes on that device was the move from Android to Tizen, an open-source mobile operating system Samsung has continued to bear the torch for on subsequent watches. The company never really looked back on that decision, even after the arrival of Android Wear.

But 2018 has found Google making a more aggressive push around its wearable operating system. I/O saw some upgrades, following a name change to Wear OS. That, along with a smattering of online rumors, point to Samsung potentially giving Googleother mobile OS a big go.

Ithard to make the case that Google has done much to warrant another look at the operating system. The smartwatch category has largely stagnated for everyone but Apple and Fitbit, and the last couple of updates haven&t brought a lot to the table. But perhaps theresomething to be said for increased compatibility across the Galaxy line.

Last yearGear Sport found Samsung offering up a more universal piece of hardware than its traditional restrictively large devices, but a ground-up rethink of the line certainly couldn&t hurt.

In a recent MIT Technology Review article, author Virginia Eubanks discusses her bookAutomating Inequality. In it, she argues that the poor are the testing ground for new technology that increases inequality— highlighting that when algorithms are used in the process of determining eligibility for/allocation of social services, it creates difficulty for people to get services, while forcing them to deal with an invasive process of personal data collection.

I&ve spoken a lot about the dangers associated with government use of face recognition in law enforcement, yet, this article opened my eyes to the unfair and potentially life threatening practice of refusing or reducing support services to citizens who may really need them — through determinations based on algorithmic data.

To some extent, we&re used to companies making arbitrary decisions about our lives — mortgages, credit card applications, car loans, etc. Yet, these decisions are based almost entirely on straightforward factors of determination — like credit score, employment and income. In the case of algorithmic determination in social services, there is bias in the form of outright surveillance in combination with forced PII share imposed upon recipients.

Eubanks gives as an example the Pittsburgh County Office of Children, Youth and Families using the Allegheny Family Screening Tool (AFST) to assess the risk of child abuse and neglect through statistical modeling. The use of the tool leads to disproportionate targeting of poor families because the data fed to the algorithms in the tool often comes from public schools, the local housing authority, unemployment services, juvenile probation services and the county police, to name just a few — basically, the data of low-income citizens who typically use these services/interact with them regularly. Conversely, data from private services such as private schools, nannies and private mental health and drug treatment services isn&t available.

Determination tools like AFST equate poverty with signs of risk of abuse, which is blatant classism — and a consequence of the dehumanization of data. Irresponsible use of AI in this capacity, like that of its use in law enforcement and government surveillance, has the real potential to ruin lives.

Taylor Owen, in his 2015 article titledThe Violence of Algorithms, described a demonstration he witnessed by intelligence analytics software company Palantir, and made two major points in response — the first being that oftentimes these systems are written by humans, based on data tagged and entered by humans, and as a result are &chock full of human bias and errors.& He then suggests that these systems are increasingly being used for violence.

&What we are in the process of building is a vast real-time, 3-D representation of the world. A permanent record of us…but where does the meaning in all this data come from& he asked, establishing an inherent issue in AI and data sets.

Historical data is useful only when it is given meaningful context, which many of these data sets are not given. When we are dealing with financial data like loans and credit cards, determinations, as I mentioned earlier, are based on numbers. While there are surely errors and mistakes made during these processes, being deemed unworthy of credit will likely not lead the police to their door.

However, a system built to predict deviancy, which uses arrest data as a main factor in determination, is not only likely to lead to police involvement — it is intended to do so.

Image courtesy of Getty Images

When we recall modern historical policies that were perfectly legal in their intention to target minority groups, Jim Crow certainly comes to mind. And letalso not forget that these laws were not declared unconstitutional until 1967, despite the Civil Rights Act of 1965.

In this context you can clearly see that according to the Constitution, Blacks have only been considered full Americans for 51 years. Currentalgorithmic biases, whether intentional or inherent, are creating a system whereby the poor and minorities are being further criminalized, and marginalized.

Clearly, there is the ethical issue around the responsibility we have as a society to do everything in our power to avoidhelping governments get better at killing people, yet the lionshare of this responsibility lies in the lap of those of us who are actually training the algorithms — and clearly, we should not be putting systems that are incapable of nuance and conscience in the position of informing authority.

In her work, Eubanks has suggested something close to a Hippocratic oath for those of us working with algorithms — an intent to do no harm, to stave off bias, to make sure that systems did not become cold, hard oppressors.

To this end, Joy Buolamwini of MIT, the founder and leader of the Algorithmic Justice League, has created a pledge to use facial analysis technology responsibly.

The pledge includes commitments like showing value for human life and dignity, which includes refusing to engage in the development of lethal autonomous weapons, and not equipping law enforcement with facial analysis products and services for unwarranted individual targeting.

This pledge is an important first step in the direction of self-regulation, which I see as the beginning of a larger grass-roots regulatory process around the use of facial recognition.