[{"innerBlocks":[],"name":"core\/freeform","postId":772,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"With the adoption of artificial intelligence by industries including healthcare, critics contend that there are inherent problems with AI which have the potential to negatively impact the lives of Americans.

Rashida Richardson, director of policy research at New York University\u2019s AI Now Institute, told a Senate subcommittee on Tuesday that the use of recommendation algorithms, predictive analytics and inferential systems are rapidly expanding and require immediate attention and action by Congress.

\u201cThough these technologies affect every American, they are primarily developed and deployed by a few powerful companies, and therefore shaped by these companies\u2019 incentives, values and interests,\u201d testified Richardson. \u201cThese companies have demonstrated limited insight into whether their products will harm consumers and even less experience in mitigating those harms. So while most technology companies promise that their products will lead to broad societal benefits, there is little evidence to support these claims. In fact, mounting evidence points to the contrary.\u201d

Also See<\/b>: Lack of AI regulatory, clinical standards pose potential risks<\/a>

Richardson pointed to IBM\u2019s Watson Health cognitive computing capabilities, which have been developed to help determine the best treatment options for patients. In particular, Watson is meant to help clinicians quickly sift through big data providing them with insights on cancer-causing mutations.

\u201cIBM\u2019s Watson supercomputer was designed to improve patient outcomes but recently internal IBM documents showed it actually provided unsafe and erroneous cancer treatment recommendations,\u201d according to Richardson. \u201cThis is just one of numerous examples that have come to light in the last year showing the difference between the marketing companies use to sell these technologies and the stark reality of how these technologies ultimately perform.\u201d

She told lawmakers that potential harms to consumers from AI systems arise from risks that current laws and incentive structures fail to adequately address, including the use of \u201cblack box\u201d technologies that prevent public transparency, accountability and oversight.

\u201cTechnologically, they are black boxes because most of the internal workings are hidden away inside the companies,\u201d Richardson observed. \u201cLegally, technology companies obstruct accountability efforts through claims of proprietary or trade secret legal protections, even though there is no evidence that legitimate inspection auditing or oversight poses any competitive risks.\u201d

However, Stephen Wolfram, founder and CEO of the computational technology firm Wolfram Research, contends that when it comes to the black box phenomenon of AI algorithms \u201cnon-explainability\u201d remains a challenge.

\u201cPeople often assume that computers just run algorithms that someone sat down and wrote, but modern AI systems don\u2019t work that way,\u201d testified Wolfram. \u201cInstead, lots of the programs they use are actually constructed automatically, usually by learning from some massive number of examples. And if you go look inside those programs, there\u2019s usually embarrassingly little that we humans can understand in there. Here\u2019s the real problem: it\u2019s sort of a fact of basic science that if you insist on explainability, then you can\u2019t get the full power of a computational system or AI.\u201d","saveContent":"With the adoption of artificial intelligence by industries including healthcare, critics contend that there are inherent problems with AI which have the potential to negatively impact the lives of Americans.

Rashida Richardson, director of policy research at New York University\u2019s AI Now Institute, told a Senate subcommittee on Tuesday that the use of recommendation algorithms, predictive analytics and inferential systems are rapidly expanding and require immediate attention and action by Congress.

\u201cThough these technologies affect every American, they are primarily developed and deployed by a few powerful companies, and therefore shaped by these companies\u2019 incentives, values and interests,\u201d testified Richardson. \u201cThese companies have demonstrated limited insight into whether their products will harm consumers and even less experience in mitigating those harms. So while most technology companies promise that their products will lead to broad societal benefits, there is little evidence to support these claims. In fact, mounting evidence points to the contrary.\u201d

Also See<\/b>: Lack of AI regulatory, clinical standards pose potential risks<\/a>

Richardson pointed to IBM\u2019s Watson Health cognitive computing capabilities, which have been developed to help determine the best treatment options for patients. In particular, Watson is meant to help clinicians quickly sift through big data providing them with insights on cancer-causing mutations.

\u201cIBM\u2019s Watson supercomputer was designed to improve patient outcomes but recently internal IBM documents showed it actually provided unsafe and erroneous cancer treatment recommendations,\u201d according to Richardson. \u201cThis is just one of numerous examples that have come to light in the last year showing the difference between the marketing companies use to sell these technologies and the stark reality of how these technologies ultimately perform.\u201d

She told lawmakers that potential harms to consumers from AI systems arise from risks that current laws and incentive structures fail to adequately address, including the use of \u201cblack box\u201d technologies that prevent public transparency, accountability and oversight.

\u201cTechnologically, they are black boxes because most of the internal workings are hidden away inside the companies,\u201d Richardson observed. \u201cLegally, technology companies obstruct accountability efforts through claims of proprietary or trade secret legal protections, even though there is no evidence that legitimate inspection auditing or oversight poses any competitive risks.\u201d

However, Stephen Wolfram, founder and CEO of the computational technology firm Wolfram Research, contends that when it comes to the black box phenomenon of AI algorithms \u201cnon-explainability\u201d remains a challenge.

\u201cPeople often assume that computers just run algorithms that someone sat down and wrote, but modern AI systems don\u2019t work that way,\u201d testified Wolfram. \u201cInstead, lots of the programs they use are actually constructed automatically, usually by learning from some massive number of examples. And if you go look inside those programs, there\u2019s usually embarrassingly little that we humans can understand in there. Here\u2019s the real problem: it\u2019s sort of a fact of basic science that if you insist on explainability, then you can\u2019t get the full power of a computational system or AI.\u201d","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

AI technology comes under fire from critics in Senate hearing

More for you