from the world's big
8 powerful voices share what it's like to be black in America, and why white people must break the racist status quo.
- Black communities have been telling the nation, for more than a century, that they have been targeted, beaten, falsely accused and killed by the police and other institutions meant to protect them.
- They have not been believed until recently, when the rise in camera phones and social media finally enabled them show and disseminate proof.
- Even after the video of George Floyd's death on May 25, 2020, there remains defensiveness and denial among white Americans and institutions—a defensiveness that prevents change to the root of the problem: systemic racism. In this video, eight powerful voices share perspectives on being black in America, and why white inaction and white politeness must end.
What makes some psychopaths better able to control their antisocial tendencies?
- Researchers have long struggled to explain the stark differences in life outcomes of psychopaths.
- A new study suggests that the personality trait conscientiousness helps psychopaths develop impulse-control skills over time.
- However, this process seems to apply only to individuals who score high in certain psychopathic traits.
'Successful' versus 'unsuccessful' psychopaths<p>The study notes that psychopathy exists on a spectrum in society, and it can manifest through a variety of personality traits, such as interpersonal manipulation, impulsivity, callousness, grandiosity, and boldness. Some traits may help psychopaths become "successful," defined as those who adapt to social norms and avoid incarceration.</p><p>For example, the psychopathic trait fearlessness may help a psychopath become a good first-responder, while interpersonal manipulation might help a psychopath become an effective lawyer. In contrast, the psychopathic trait impulsivity may make a psychopath more likely to commit crime.</p><p>The researchers hypothesized that psychopaths who are able develop impulse-control skills are more likely to be successful. The team suggested that successful psychopaths develop a mechanism that gives them greater control over their behavior, helping them thwart their heightened antisocial impulses.</p><p>Conscientiousness is the trait that predicts whether a psychopath will develop this mechanism, according to the study.</p><p style="margin-left: 20px;">"The compensatory model posits that people higher in certain psychopathic traits (such as grandiosity and manipulation) are able to compensate for and overcome, to some extent, their antisocial impulses via increases in trait conscientiousness, specifically impulse control," Lasko said.</p>
Lasko et al.<p>To test the hypothesis, the researchers examined data from a seven-year longitudinal study on adolescent criminals in Arizona and Pennsylvania.<br></p><p style="margin-left: 20px;">"Although these participants are not objectively 'successful,' this was an ideal sample to test our hypotheses for two main reasons," the researchers wrote. "First, adolescents are in a prime developmental phase for the improvement of impulse control. Allowing us the longitudinal variability we would need to test our compensatory model. Second, offenders are prone to antisocial acts, by definition, and their rates of recidivism provided a real-world index of 'successful' versus 'unsuccessful' psychopathy phenotypes."</p><p>The study found that adolescents who scored high in grandiose-manipulative psychopathic traits early in the study were more likely to develop better impulse control and less aggression over time. Psychopaths who scored higher in impulsivity didn't see as much of an increase.</p>
Over 800 prisoners in Texas relate their experiences.
Can AI make better predictions about future crimes?
- A new study finds algorithmic predictions of recidivism more accurate than human authorities.
- Researchers are trying to construct tests of such AI that accurately mirror real-world deliberations.
- What level of reliability should we demand of AI in sentencing?
RAIs, NG?<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yMjc3MzAwMC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxMDQxMjM4N30.ByZIs2U0SqXx4bf6u9LvqCAk-rwUuM33ClPkxuQhEk8/img.jpg?width=980" id="aa986" class="rm-shortcode" data-rm-shortcode-id="30eabf6c00ea57d1257274d16330fd0d" data-rm-shortcode-name="rebelmouse-image" />
Image source: Andrey Suslov/Shutterstock<p>The new study, led by computational social scientist <a href="https://5harad.com" target="_blank">Sharad Goel</a> of Stanford University, is in a sense a reply to a <a href="https://advances.sciencemag.org/content/4/1/eaao5580?ijkey=acb268ff69558c41f4083ae815a5c7a262232a5d&keytype2=tf_ipsecsha" target="_blank">recent work</a> by programming expert Julia Dressel and digital image specialist Hany Farid. In that earlier research, participants attempted to predict whether or not any of 50 individuals would commit new crimes of any kind within the next two years based on short descriptions of their case histories. (No images or racial/ethnic information were provided to participants to avoid a skewing of results due to related biases.) The average accuracy rate participants achieved was 62%.</p><p>The same criminals and case histories cases were also processed through a widely used RAI called COMPAS, for "Correctional Offender Management Profiling for Alternative Sanctions." The accuracy of its predictions was about the same: 65%, leading Dressel and Farid to conclude that COMPAS "is no more accurate … than predictions made by people with little or no criminal justice expertise."</p>
Taking a second look<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yMjc3MzAwNi9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY0MDg4MjA1MX0._jJsmArbQFUQquUG5Hnpu8xLaXG0qxSQa1zRx2GcL-E/img.jpg?width=980" id="c6964" class="rm-shortcode" data-rm-shortcode-id="69dbbb9244e916e4e4e2bf1a7efb6189" data-rm-shortcode-name="rebelmouse-image" /><p>Goel felt that two aspects of the testing method used by Dressel and Farid didn't reproduce closely enough the circumstances in which humans are called upon to predict recidivism during sentencing:</p><ol><li>Participants in that study learned how to improve their predictions, much as an algorithm might, as they were provided feedback as to the accuracy of each prognostication. However, as Goel points out, "In justice settings, this feedback is exceedingly rare. Judges may never find out what happens to individuals that they sentence or for whom they set bail."</li><li>Judges, etc. also often have a great deal of information in hand as they make their predictions, not short summaries in which only the most salient information is presented. In the real world, it can be hard to ascertain which information is the most relevant when there's arguably too much of it at hand.</li></ol><p>Both of these factors put participants on a more equal footing with an RAI than they would be in real life, perhaps accounting for the similar levels of accuracy encountered.</p><p>To that end, Goel and his colleagues performed several of their own, slightly different, trials.</p><p>The first experiment closely mirrored Dressel's and Farid's — with feedback and short case descriptions — and indeed found that humans and COMPAS performed pretty much equally well. Another experiment asked participants to predict the future occurrence of <em>violent</em> crime, not just any crime, and again the accuracy rates were comparable, though much higher. Humans scored 83% as COMPAS achieved 89% accuracy.</p><p>When participant feedback was removed, however, humans fell far behind COMPAS in accuracy, down to around 60% as opposed to COMPAS's 89%, as Goel hypothesized they might.</p><p>Finally, humans were tested against a different RAI tool called LSI-R. In this case, both had to try and predict an individual's future using on a large amount of case information similar to what a judge may have to wade through. Again, the RAI outperformed humans in predicting future crimes, 62% to 57%. When asked to predict who would wind up going back to prison for their future misdeeds, the results were even worse for participants, who got it right just 58% of the time as opposed to 74% for LSI-R.</p>
Good enough?<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yMjc3MzAxNS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyNzk5MTc5OH0.kq0yWKlclL3emX-xxqeLxN53v1czSUhKBDEmglY6VZ0/img.jpg?width=980" id="b3c58" class="rm-shortcode" data-rm-shortcode-id="d00f0ba7c26d9fdada23607448ffdc33" data-rm-shortcode-name="rebelmouse-image" />
Image source: klss/Shutterstock<p>Goel concludes, "our results support the claim that algorithmic risk assessments can often outperform human predictions of reoffending." Of course, this isn't the only important question. There's also this: Is AI yet reliable enough to make its prediction count for more than that of a judge, correctional authority, or parole board member?</p><p><a href="https://www.sciencenews.org/article/ai-can-predict-criminals-repeat-offenders-better-than-humans" target="_blank"><em>Science News</em></a> asked Farid, and he said no. When asked how he'd feel about an RAI that could be counted on to be right 80% of the time, he responded, "you've got to ask yourself, if you're wrong 20 percent of the time, are you willing to tolerate that?"</p><p>As AI technology improves, we may one day reach a state in which RAIs are reliably accurate, but no one is claiming we're there yet. For now, then, the use of such technologies in an advisory role for authorities tasked with making sentencing decisions may make sense, but only as one more "voice" to consider.</p>
Scholars often debate risking their livelihoods and personal safety in order to conduct research in certain areas.
- Authoritarian governments that rely heavily on coercion must be more intrusive about how education shapes the personality and character of its members.
- In China, there are topics that scholars know to avoid — especially, the Three Ts: Taiwan, Tibet, and Tiananmen Square.
- While the majority of scholars are likely toeing the party line when it comes to their research, some are working toward encouraging academic freedom in the country, often at significant risk to themselves and their families.
President Xi Jinping inspects the Chinese People's Liberation Army Garrison In Hong Kong.