Averting Robot Eyes

Home robots will cause privacy harms. At the same time, they can provide beneficial services—as long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms. We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology

Picture Composition for a Robot Photographer

We explain how to use simple composition rules to drive an automated, mobile photography system. The composition rules are used to determine both the location for a good photograph, and how to frame that photograph. We describe the composition component in the context of a larger application, a robotic photographer. The robot moves around an area with people in it, opportunistically looking for faces and taking photographs. We describe both how to find faces in the world and how to create “good” photographs of those faces

Averting Robot Eyes

Home robots will cause privacy harms. At the same time, they can provide beneficial services—as long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms. We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology

An Education Theory of Fault For Autonomous Systems

Automated systems like self-driving cars and “smart” thermostats are a challenge for fault-based legal regimes like negligence because they have the potential to behave in unpredictable ways. How can people who build and deploy complex automated systems be said to be at fault when they could not have reasonably anticipated the behavior (and thus risk) of their tools?Part of the problem is that the legal system has yet to settle on the language for identifying culpable behavior in the design and deployment for automated systems. In this article we offer an education theory of fault for autonomous systems—a new way to think about fault for all the relevant stakeholders who create and deploy “smart” technologies. We argue that the most important failures that lead autonomous systems to cause unpredictable harm are due to the lack of communication, clarity, and education between the procurer, developer, and users of these technologies.In other words, while it is hard to exert meaningful control over automated systems to get them to act predictably, developers and procurers have great control over how much they test these tools and articulate their limits to all the other relevant parties. This makes testing and education one of the most legally relevant point of failures when automated systems harm people. By recognizing a responsibility to test and educate each other, foreseeable errors can be reduced, more accurate expectations can be set, and autonomous systems can be made more predictable and safer

Whole genome sequencing reveals a 7 base-pair deletion in DMD exon 42 in a dog with muscular dystrophy

Dystrophin is a key cytoskeletal protein coded by the Duchenne muscular dystrophy (DMD) gene located on the X-chromosome. Truncating mutations in the DMD gene cause loss of dystrophin and the classical DMD clinical syndrome. Spontaneous DMD gene mutations and associated phenotypes occur in several other species. The mdx mouse model and the golden retriever muscular dystrophy (GRMD) canine model have been used extensively to study DMD disease pathogenesis and show efficacy and side effects of putative treatments. Certain DMD gene mutations in high-risk, the so-called hot spot areas can be particularly helpful in modeling molecular therapies. Identification of specific mutations has been greatly enhanced by new genomic methods. Whole genome, next generation sequencing (WGS) has been recently used to define DMD patient mutations, but has not been used in dystrophic dogs. A dystrophin-deficient Cavalier King Charles Spaniel (CKCS) dog was evaluated at the functional, histopathological, biochemical, and molecular level. The affected dog’s phenotype was compared to the previously reported canine dystrophinopathies. WGS was then used to detect a 7 base pair deletion in DMD exon 42 (c.6051-6057delTCTCAAT mRNA), predicting a frameshift in gene transcription and truncation of dystrophin protein translation. The deletion was confirmed with conventional PCR and Sanger sequencing. This mutation is in a secondary DMD gene hotspot area distinct from the one identified earlier at the 5′ donor splice site of intron 50 in the CKCS breed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00335-016-9675-2) contains supplementary material, which is available to authorized users