While the COVID-19 crisis got most folks thinking about face masks and toilet paper, Chris Calabrese was pondering artificial intelligence and its implications for public policy. His aha moment came when he realized Facebook had sent home most of its human overseers and put AI in charge of policing the social forum for inappropriate content. "The result has been systems that don't work as well. They are taking down groups dedicated to sewing masks, just because they are falsely flagged," said Calabrese, vice president of policy at the Center for Democracy and Technology. "That's automation being used by one of the most influential companies in the world, and it's still not up to snuff. That gives me a sense of how far we have to go." Facebook's stuttering steps into automation reflect broader ethical challenges faced by public tech leaders as AI, biometrics and surveillance technologies increasingly enter the mainstream.

For years, the Environmental Protection Agency's regulation of radiation, carcinogens, and other toxic chemicals has been based on the cautious scientific reasoning that considers even slight exposure to toxins potentially risky to public health. From that premise, the EPA has assessed a wide range of pollution, including lung-clogging particulate matter, Superfund cleanup, water treatment, radiation exposure, and risk assessments for carcinogens like benzene. That time-honored approach may be changing because of easy-to-overlook phrasing within a paragraph buried in the proposed "Strengthening Transparency In Regulatory Science Rule," a regulation that will bar the EPA from considering a wide range of scientific studies in its rule-making. With a few sentences buried in the seven-page Federal Register text, the EPA is opening the door to a new scientific approach that--in a worst-case scenario--could further relax regulations because of the assumption that a little pollution is actually beneficial. Some scientists have considered the implications of this paragraph and described a whole array of potential problems to Mother Jones. Because the paragraph is written in incredibly vague language, most scientists were unable to explain which pollutants or regulations were the prime targets.

For years, the Environmental Protection Agency's regulation of radiation, carcinogens, and other toxic chemicals has been based on the cautious scientific reasoning that considers even slight exposure to toxins potentially risky to public health. From that premise, the EPA has assessed a wide range of pollution, including lung-clogging particulate matter, Superfund cleanup, water treatment, radiation exposure, and as well as risk assessments for carcinogens like benzene. That time-honored approach may be changing because of easy-to-overlook phrasing within a paragraph buried in the proposed "Strengthening Transparency In Regulatory Science Rule," a regulation that will bar the EPA from considering a wide range of scientific studies in its rule making. With a few sentences buried in the seven-page Federal Register text, the EPA is opening the door to a new scientific approach that--in a worst-case scenario--could further relax regulations because of the assumption that a little pollution is actually beneficial. Some scientists have considered the implications of this paragraph and described a whole array of potential problems to Mother Jones. Written in incredibly vague language, most scientists were unable to explain which pollutants or regulations were the prime target.

Growing pool of public-generated bits like online social networking data provides possibility to sense social dynamics in the urban space. In this position paper, we use a location-based online social networking data to sense geo-social activity and analyze the underlying social activity distribution of three different cities: London, Paris, and New York. We find a non-linear distribution of social activity, which follows the Power Law decay function. We perform inter-urban analysis based on social activity distribution and clustering. We believe that our study sheds new light on context-aware urban computing and social sensing.

We have developed a VLSI silicon neuron and a corresponding mathematical model that is a two state-variable system. We describe the circuit implementation and compare the behaviors observed in the silicon neuron and the mathematical model. We also perform bifurcation analysis of the mathematical model by varying the externally applied current and show that the behaviors exhibited by the silicon neuron under corresponding conditions are in good agreement to those predicted by the bifurcation analysis.

We have developed a VLSI silicon neuron and a corresponding mathematical model that is a two state-variable system. We describe the circuit implementation and compare the behaviors observed in the silicon neuron and the mathematical model. We also perform bifurcation analysis of the mathematical model by varying the externally applied current and show that the behaviors exhibited by the silicon neuron under corresponding conditions are in good agreement to those predicted by the bifurcation analysis.

We have developed a VLSI silicon neuron and a corresponding mathematical modelthat is a two state-variable system. We describe the circuit implementation and compare the behaviors observed in the silicon neuron and the mathematical model. We also perform bifurcation analysis ofthe mathematical model by varying the externally applied current and show that the behaviors exhibited by the silicon neuron under corresponding conditionsare in good agreement to those predicted by the bifurcation analysis.