She would be well-advised to donate many times her bribery payment to scholarships for underprivileged students, either helping to pay their tuition, or covering the costs of things like SAT prep classes that give the rich yet another advantage.
I would encourage her to use her community service hours to benefit children whose opportunities are constrained through no fault of their own—not necessarily tutoring all students deserve qualified teachers, not just a sentient adult in the room , but perhaps gardening, landscaping, cleaning and painting at an under-resourced school, so that more children can learn in the kind of clean, calm, and attractive environment Felicity Huffman chose for her own kids.
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Campaign Cap. By Kenzie Bryant. Each NJS included several public opinion research projects with Canadians 18 and over from across Canada.
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The NJS included two surveys surveys 1 and 2 , six in-person focus groups and three online discussions. The NJS included two surveys surveys 1 and 2 , twelve in-person focus groups and twenty one-on-one telephone interviews. Survey samples were drawn randomly and the surveys were completed online or via paper.
The data were weighted on age, gender, geographic region and education to match the Canadian population.
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This shift towards more machine intelligence in courts, allowing AI to augment human judgement, could be extremely beneficial for the judicial system as a whole. However, an investigative report by ProPublica found that these algorithms tend to reinforce racial bias in law enforcement data. Algorithmic assessments tend to falsely flag black defendants as future criminals at almost twice the rate as white defendants.
What is more, the judges who relied on these risk-assessments typically did not understand how the scores were computed. If the underlying data is biased in any form, there is a risk that structural inequalities and unfair biases are not just replicated, but also amplified. In this regard, AI engineers must be especially wary of their blind spots and implicit assumptions; it is not just the choice of machine learning techniques that matters, but also all the small decisions about finding, organising and labelling training data for AI models.
Even small irregularities and biases can produce a measurable difference in the final risk-assessment.
Next two sentences will test case further
The critical issue is that problems like racial bias and structural discrimination are baked into the world around us. For instance, there is evidence that, despite similar rates of drug use, black Americans are arrested at four times the rate of white Americans on drug-related charges. Even if engineers were to faithfully collect this data and train a machine learning model with it, the AI would still pick up the embedded bias as part of the model. Systematic patterns of inequality are everywhere.
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New machine learning models can quantify these inequalities , but there are a lot of open questions about how engineers can proactively mitigate them. The experiment invited internet users worldwide to participate in a fun game of drawing.
In every round of the game, users were challenged to draw an object in under 20 seconds. The AI system would then try to guess what their drawing depicts.
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More than 20 million people from nations participated in the game, resulting in over 2 billion diverse drawings of all sorts of objects, including cats, chairs, postcards, butterflies, skylines, etc. But when the researchers examined the drawings of shoes in the data-set, they realised that they were dealing with strong cultural bias. A large number of early users drew shoes that looked like Converse sneakers. Consequently, shoes that did not look like sneakers , such as high heels, ballerinas or clogs, were not recognized as shoes.
In a similar fashion, AI models trained on images of past US presidents have been shown to predict exclusively male candidates as the likely winner of the presidential race.
In October , the International Conference of Data Protection and Privacy Commissioners released the Declaration on Ethics and Protection in Artificial Intelligence , one of the first steps towards a set of international governance principles on AI. Inherent to this notion is the assertion that AI needs to be evaluated on a broader set of ethical and legal criteria; not just based on classification accuracy and confusion matrices.
Expanding on this argument, I propose the following principles of AI fairness for the purposes of predictive justice:. In order to guard against unfair bias, all subjects should have an equal chance of being represented in the data. Sometimes this means that underrepresented populations need to be thoughtfully added to any training datasets. Sometimes this also means that a biased machine learning model needs to be substantially retrained on diverse data sources.