Machine learning is a powerful and imperfect tool that should not go unmonitored.
- When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage.
- Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque.
- In this article, I cover six ways that machine learning threatens social justice and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism.
Here are six ways machine learning threatens social justice<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDUyMDgxNC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY0MzM0NjgxOH0.zHvEEsYGbNA-lnkq4nss7vwVkZlrKkuKf0XASf7A7Jg/img.jpg?width=980" id="05f07" class="rm-shortcode" data-rm-shortcode-id="a7089b6621166f5a2df77d975f8b9f74" data-rm-shortcode-name="rebelmouse-image" data-width="1000" data-height="563" />
Credit: metamorworks via Shutterstock<p><strong></strong><strong>1) </strong><strong>Blatantly discriminatory models</strong> are predictive models that base decisions partly or entirely on a protected class. Protected classes include race, religion, national origin, gender, gender identity, sexual orientation, pregnancy, and disability status. By taking one of these characteristics as an input, the model's outputs – and the decisions driven by the model – are based at least in part on membership in a protected class. Although models rarely do so directly, there is <a href="https://www.youtube.com/watch?v=eSlzy1x6Fy0" target="_blank">precedent</a> and <a href="https://www.youtube.com/watch?v=wfpNN8ASIq4" target="_blank">support</a> for doing so.</p><p>This would mean that a model could explicitly hinder, for example, black defendants for being black. So, imagine sitting across from a person being evaluated for a job, a loan, or even parole. When they ask you how the decision process works, you inform them, "For one thing, our algorithm penalized your score by seven points because you're black." This may sound shocking and sensationalistic, but I'm only literally describing what the model would do, mechanically, if race were permitted as a model input. </p><p><strong>2) Machine bias</strong>. Even when protected classes are not provided as a direct model input, we find, in some cases, that model predictions are still inequitable. This is because other variables end up serving as proxies to protected classes. This is <a href="https://coursera.org/share/51350b8fb12a5937bbddc0e53a4f207d" target="_blank" rel="noopener noreferrer">a bit complicated</a>, since it turns out that models that are fair in one sense are unfair in another. </p><p>For example, some crime risk models succeed in flagging both black and white defendants with equal precision – each flag tells the same probabilistic story, regardless of race – and yet the models falsely flag black defendants more often than white ones. A crime-risk model called COMPAS, which is sold to law enforcement across the US, falsely flags white defendants at a rate of 23.5%, and Black defendants at 44.9%. In other words, black defendants who don't deserve it are <a href="https://coursera.org/share/df6e6ba7108980bb7eeae0ba22123ac1" target="_blank" rel="noopener noreferrer">erroneously flagged almost twice as much</a> as white defendants who don't deserve it.</p><p><strong>3) Inferring sensitive attributes</strong>—predicting pregnancy and beyond. Machine learning predicts sensitive information about individuals, such as sexual orientation, whether they're pregnant, whether they'll quit their job, and whether they're going to die. Researchers have shown that it is possible to <a href="https://youtu.be/aNwvXhcq9hk" target="_blank" rel="noopener noreferrer">predict race based on Facebook likes</a>. These predictive models deliver dynamite.</p><p>In a particularly extraordinary case, officials in China use facial recognition to <a href="https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html" target="_blank" rel="noopener noreferrer">identify and track the Uighurs, a minority ethnic group</a> systematically oppressed by the government. This is the first known case of a government using machine learning to profile by ethnicity. One Chinese start-up valued at more than $1 billion said its software could recognize "sensitive groups of people." It's website said, "If originally one Uighur lives in a neighborhood, and within 20 days six Uighurs appear, it immediately sends alarms" to law enforcement.</p>
Recourse: Establish machine learning standards as a form of social activism<p>To address these problems, take on machine learning standardization as a form of social activism. We must establish standards that go beyond nice-sounding yet vague platitudes such as "be fair", "avoid bias", and "ensure accountability". Without being precisely defined, these catch phrases are subjective and do little to guide concrete action. Unfortunately, such broad language is fairly common among the principles released by many companies. In so doing, companies protect their public image more than they protect the public.<br></p><p>People involved in initiatives to deploy machine learning have a powerful, influential voice. These relatively small numbers of people mold and set the trajectory for systems that automatically dictate the rights and resources that great numbers of consumers and citizens gain access to.</p><p>Famed machine learning leader and educator Andrew Ng drove it home: "AI is a superpower that enables a small team to affect a huge number of people's lives... Make sure the work you do leaves society better off."</p><p>And Allan Sammy, Director, Data Science and Audit Analytics at Canada Post, clarified the level of responsibility: "A decision made by an organization's analytic model is a decision made by that entity's senior management team."</p><p>Implementing ethical data science is as important as ensuring a self-driving car knows when to put on the breaks.</p><p>Establishing well-formed ethical standards for machine learning will be an intensive, ongoing process. For more, <a href="https://youtu.be/ToSj0ZkJHBQ" target="_blank">watch this short video</a>, in which I provide some specifics meant to kick-start the process.</p>
Antisocial is a deep dive into the extremist views.
- The New Yorker's Adam Marantz spent three years embedded with leading alt-right voices.
- His book, Antisocial, carries you deep inside the mindset and motivation behind online trolling.
- To get back on track, Marantz believes we need a "new moral vocabulary."
Inside the bizarre world of internet trolls and propagandists | Andrew Marantz<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="242633e374cd2b8987ea5f1946d2e6ea"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/2ix8JEqCJ1s?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p>If you think there's a coherent plan behind this overrepresented minority broadcasting on Twitter, Facebook, Periscope, and YouTube, rethink that assumption. Marantz begins his book at the DeploraBall, an unofficial inaugural celebration organized by alt-right conspiracy theorists and internet trolls in 2017. Commenting on the movement from the bird's-eye view, Marantz sums up the motivation behind the political momentum that placed Donald Trump into office. </p><p style="margin-left: 20px;">"They took for granted that the old institutions ought to be burned to the ground, and they used the tools at their disposal—new media, especially social media—to light as many matches as possible. As for what kind of society might emerge from the ashes, they had no coherent vision and showed little interest in developing one. They were not, like William Buckley, standing athwart history, yelling 'Stop'; they were holding liberal democracy in a headlock, yelling 'Stop or I'll shoot!'" </p><p>Marantz does his best to sympathize with the characters he writes about, a commendable feat in itself. He approaches reporting in what is now considered an old school style: credibility. He didn't accept gifts (including Uber rides or coffee), allowed his subjects to speak their voices, and asked pointed questions while letting them speak their grievances. Indeed, the strongest parts of the book, and ironically the most frustrating, occur when you're in the living room of one of these aspirational provocateurs as they play with their children. </p><p>Frustrating because, as with Twitter fights and trolling in general, you're reminded that all of us share one nation. We have the capability to be so much better than this. Yet debatable policy disagreements are regularly broadcast as existential threats for clickbait to drive ad revenue. The real focus of our collective anger, corporate leaders and the politicians they purchase, own much of the blame for this polarization. It just seems impossible to remember that fact while scrolling on a six-inch screen. </p><p>That said, Marantz does not give a free pass to the white nationalist movement. Being Jewish, he recognized the personal danger he placed himself in. Marantz also considers the role of the modern journalist. He might pay for breakfast to avoid conflicts of interest, but that doesn't make supporting leaders of this movement easy. Some ideologies simply do not bend toward justice.</p><p style="margin-left: 20px;">"To treat these as legitimate topics of debate is to be not neutral but complicit. Sometimes, even for a journalist, there is no such thing as not picking a side."</p>
Andrew Marantz (via Twitter)<p>King's quote is a recurring theme throughout the book; so is the Overton window. Named after Joseph P. Overton, a former senior VP of the Mackinac Center for Public Policy, this window is the range of policies a politician can discuss without appearing too extreme or biased. The window shifts as we become inoculated to more extreme ideas. What seemed impossible a decade ago becomes common. You get an open discussion of racist and xenophobic policies that would have once seemed unthinkable.</p><p>Don't mistake this window for critical thinking. If, at times, it feels like social media is ruled by emotionally incompetent and intellectually stymied adults who never took the opportunity to mature from grade school, you're not far off. Sometimes all Marantz has to do is stick a microphone in front of their mouths and let them speak. It's maddening, listening to them shrug off thoughtfulness and honest debate. Defaulting to "free speech," which they all do, is to forget (or be ignorant of) the fact that with free speech comes responsibility.</p><p>We cannot troll our way out of this mess. As Marantz concludes, we need a "new moral vocabulary" to address the scourge of anti-Semitic, racist, and xenophobic garbage being lightly disguised (or not at all) in our national discourse. I purposely avoided naming the figures in his book because they already receive too much oxygen. One high point is that many have been de-platformed in recent years, cutting off their precious revenue streams. </p><p>No book has captured the alt-right as powerfully and honestly as <em>Antisocial</em>. It is a reminder of how badly we need to redefine the Overton window with a new vocabulary. Teaching everyone this language will be one of our greatest challenges in this new decade. <span></span></p><p>--</p><p><em>Stay in touch with Derek on <a href="http://www.twitter.com/derekberes" target="_blank">Twitter</a> and <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank">Facebook</a>. His next book is </em>Hero's Dose: The Case For Psychedelics in Ritual and Therapy.</p>
Study identifies predictors of which students are likely to do well in education.
- Researchers looked at data from 5,000 students and found 2 factors that were strongly linked to academic success.
- Students with genetic predisposition towards academics were much more likely to go to University.
- Equally important was having well-educated parents with wealth.
How can we best help students? Cultivate their love for learning.<div class="rm-shortcode" data-media_id="TgNt03ck" data-player_id="FvQKszTI" data-rm-shortcode-id="f677c0e6cef50e09d6131d19cc289de8"> <div id="botr_TgNt03ck_FvQKszTI_div" class="jwplayer-media" data-jwplayer-video-src="https://content.jwplatform.com/players/TgNt03ck-FvQKszTI.js"> <img src="https://cdn.jwplayer.com/thumbs/TgNt03ck-1920.jpg" class="jwplayer-media-preview" /> </div> <script src="https://content.jwplatform.com/players/TgNt03ck-FvQKszTI.js"></script> </div>
Transformation of big companies is really important if we want to create a system that is fairer, more sustainable and less unequal.
- Large companies can and should ask themselves "Where can we collaborate? Where can we pre-compete?"
- Both collaboration and competition can help big business be the force for good.
- B Corp certifications can lead to companies being more purposeful and transparent.