Susan Fahringer's guest appearances

Susan Fahringer

Finding Genius Podcast

J Scott

Bill Barhydt

Whitney Johnson

Alex Mashinsky

Ross Ulbricht

Lyn Ulbricht

Jameson Lopp

Marco Peereboom

Megan Ramos

Danny Flood

Abel James

Nir Eyal

Perry Marshall

Scott Young

Ben Goertzel

OwlTail

Perkins Coie

Susan Fahringer, a partner at Perkins Coie (perkinscoie.com), an international law firm specializing in corporate, commercial litigation, intellectual property, and regulatory legal advice, gives an overview of the current and coming legal issues regarding AI. The legal issues surrounding AI (artificial intelligence) are poised to be some of the most critical and pertinent areas of concern facing many companies and persons involved in the rapidly growing technology arena.  Fahringer informs that biometrics legislation; algorithmic discrimination, which pertains to employers’ use of AI to discriminate in hiring practices; privacy issues; product liability issues; and anti-trust issues will be a few of the many legal areas of concern in the expanding AI space. Specifically, in regard to biometrics, questions arise regarding personal data and what those who collect it are actually allowed to do with it. While biometrics can be useful on many fronts, Fahringer states that there should certainly be some guardrails established regarding its collection and use.  Fahringer discusses the problems that employers could face legally if their machine learning algorithms are biased. For example, if an employer creates an algorithm to search for potential candidates to interview and subsequently hire, the algorithm needs to be unbiased in every way such that it does not exclude or discriminate against people based on inherent flaws in the algorithm. Race is an obvious area where bias concerns could arise, and employers should take steps to ensure that their algorithms do no use race as a determinant in any way. But another area that may not be as obvious to employers is their use of zip codes, for an algorithm that discriminates due to a particular zip code, whether purposefully or inadvertently, may also be in violation of anti-discrimination laws.  Fahringer discusses the many areas of potential legal impediments and problems that exist within the current GDPR (General Data Protection Regulation). She outlines the importance of balance within the areas that concern privacy, balance that allows for expanded innovation but still protects the privacy of the individual. Fahringer expects that it will take five to ten years to refine the various laws that exist currently regarding AI, as the field and the laws that surround it are constantly evolving. Fahringer states that disclosures, privacy and data security, product liability, risk, and employment issues will all be areas of increasing concern for manufacturers, consumers, and private individuals alike.

Susan Fahringer, a partner at Perkins Coie (<a href="http://perkinscoie.com" rel="nofollow" target="_blank">perkinscoie.com</a>), an international law firm specializing in corporate, commercial litigation, intellectual property, and regulatory legal advice, gives an overview of the current and coming legal issues regarding AI. The legal issues surrounding AI (artificial intelligence) are poised to be some of the most critical and pertinent areas of concern facing many companies and persons involved in the rapidly growing technology arena.  Fahringer informs that biometrics legislation; algorithmic discrimination, which pertains to employers’ use of AI to discriminate in hiring practices; privacy issues; product liability issues; and anti-trust issues will be a few of the many legal areas of concern in the expanding AI space. Specifically, in regard to biometrics, questions arise regarding personal data and what those who collect it are actually allowed to do with it. While biometrics can be useful on many fronts, Fahringer states that there should certainly be some guardrails established regarding its collection and use.  Fahringer discusses the problems that employers could face legally if their machine learning algorithms are biased. For example, if an employer creates an algorithm to search for potential candidates to interview and subsequently hire, the algorithm needs to be unbiased in every way such that it does not exclude or discriminate against people based on inherent flaws in the algorithm. Race is an obvious area where bias concerns could arise, and employers should take steps to ensure that their algorithms do no use race as a determinant in any way. But another area that may not be as obvious to employers is their use of zip codes, for an algorithm that discriminates due to a particular zip code, whether purposefully or inadvertently, may also be in violation of anti-discrimination laws.  Fahringer discusses the many areas of potential legal impediments and problems that exist within the current GDPR (General Data Protection Regulation). She outlines the importance of balance within the areas that concern privacy, balance that allows for expanded innovation but still protects the privacy of the individual. Fahringer expects that it will take five to ten years to refine the various laws that exist currently regarding AI, as the field and the laws that surround it are constantly evolving. Fahringer states that disclosures, privacy and data security, product liability, risk, and employment issues will all be areas of increasing concern for manufacturers, consumers, and private individuals alike.

Legal Leviathan – Susan Fahringer, Partner, Perkins Coie – A Practical Discussion of the Dizzying Array of Laws in the Artificial Intelligence Industry 