Archive for September, 2018

Unbiased algorithms can still be problematic

Creating unbiased, accurate algorithms isn’t impossible — it’s just time consuming. “It actually is mathematically possible,” facial recognition startup Kairos CEO Brian Brackeen told me on a panel at TechCrunch Disrupt SF. Algorithms are sets of rules that computers follow in order to solve problems and make decisions about a particular course of action. Whether it’s the type of information we receive, the information people see about us, the jobs we get hired to do, the credit cards we get approved for, and, down the road, the driverless cars that either see us or don’t, algorithms are increasingly becoming a big part of our lives. But there is an inherent problem with algorithms that begins at the most base level and persists throughout its adaption: human bias that is baked into these machine-based decision-makers. Creating unbiased algorithms is a matter of having enough accurate data. It’s not about just having enough “pale males” in the model, but about having enough images of people from various racial backgrounds, genders, abilities, heights, weights and so forth. Kairos CEO Brian Brackeen “In our world, facial recognition is all about human biases, right?” Brackeen said. “And so you think about AI, it’s learning, it’s like a child and you teach it things and then it learns more and more. What we call right down the middle, right down the fair way is ‘pale males.’ It’s very, very good. Very, very good at identifying somebody who meets that classification.” But the further you get from pale males — adding women, people from different ethnicities, and so forth — “the harder it is for AI systems to get it right, or at least the confidence to get it right,” Brackeen said. Still, there are cons to even a one hundred percent accurate model. On the pro side, a good facial recognition use case for a completely accurate algorithm would be in a convention center, where you use the system to quickly identity and verify people are who they say they are. That’s one type of use case Kairos, which works with corporate businesses around authentication, addresses. “So if we’re wrong, at worst case, maybe you have to do a transfer again to your bank account,” he said. “If we’re wrong, maybe you don’t see a picture accrued during a cruise liner. But when the government is wrong about facial recognition, and someone’s life or liberty is at stake, they can be putting you in a lineup that you shouldn’t be in. They could be saying that this person is a criminal when they’re not.” But in the case of law enforcement, no matter how accurate and unbiased these algorithms are, facial recognition software has no business in law enforcement, Brackeen said. That’s because of the potential for unlawful, excessive surveillance of citizens. Given the government already has our passport photos and identification photos, “they could put a camera on Main Street and know every single person driving by,” Brackeen said. And that’s a real possibility. In the last month, […]

The war over music copyrights

VC firms haven’t been the only ones raising hundreds of millions of dollars to invest in a booming market. After 15+ years of being the last industry anyone wanted to invest in, the music industry is coming back, and money is flooding in to buy up the rights to popular songs. As paid streaming subscriptions get mainstream adoption, the big music streaming services – namely Spotify, Apple Music, and Tencent Music, but also Pandora, Amazon Music, YouTube Music, Deezer, and others – have entered their prime. There are now over 51 million paid subscription accounts among music streaming services in the US. The music industry grew 8% last year globally to $17.3 billion, driven by a 41% increase in streaming revenue and 45% increase in paid streaming revenue. The surge in music streaming means a surge in income for those who own the copyrights to songs, and the growth of entertainment in emerging markets, growing use in digital videos, and potential use of music in new content formats like VR only expand this further. Unsurprisingly, private equity firms, family offices, corporates, and pension funds want a piece of the action. There are two general types of copyrights for a song: the publishing rights and the master rights. The musical composition of a song – the lyrics, melodies, etc. – comes from songwriters who own the publishing right (though generally they sign a publishing deal and their publisher gets ownership of it in addition to half the royalties). Meanwhile, the version of a song being performed comes from the recording artist who owns the master right (though usually they sign a record deal and the record label gets ownership of the masters and most of the royalties). Popular songs are valuable to own because of all the royalties they collect: whenever the song is played on a streaming service, downloaded from iTunes, or covered on YouTube (a mechanical license), played over radio or in a grocery store (a performance license), played as soundtrack over a movie or TV show (a sync license), and for other uses. More royalty income from a song goes to the master owner since they took on more financial risk marketing it, but publishers collect royalties from some channels that master owners don’t (like radio play, for instance). For a songwriter behind popular songs, these royalties form a predictable revenue stream that can amount to tens of thousands, hundreds of thousands, or even millions of dollars per year. Of course, most songs that are written or recorded don’t make any money: creating a track that breaks out in a crowded industry is hard. This scarcity – there are only so many thousands of popular musicians and a limited number of legendary artists whose music stays relevant for decades – means copyrights for successful musicians command a premium when they or their publisher decide to sell them. Investing in streaming economics In 2017, revenue from streaming services accounted for 38% of worldwide music industry revenue, finally overtaking revenue […]