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Why are Artificial Intelligence systems biased?

#artificialintelligence

A machine-learned AI system used to assess recidivism risks in Broward County, Fla., often gave higher risk scores to African Americans than to whites, even when the latter had criminal records. The popular sentence-completion facility in Google Mail was caught assuming that an "investor" must be a male. A celebrated natural language generator called GPT, with an uncanny ability to write polished-looking essays for any prompt, produced seemingly racist and sexist completions when given prompts about minorities.


Why are Artificial Intelligence systems biased? – IAM Network

#artificialintelligence

A machine-learned AI system used to assess recidivism risks in Broward County, Fla., often gave higher risk scores to African Americans than to whites, even when the latter had criminal records. The popular sentence-completion facility in Google Mail was caught assuming that an "investor" must be a male.A celebrated natural language generator called GPT, with an uncanny ability to write polished-looking essays for any prompt, produced seemingly racist and sexist completions when given prompts about minorities. Amazon found, to its consternation, that an automated AI-based hiring system it built didn't seem to like female candidates.Commercial gender-recognition systems put out by industrial heavy-weights, including Amazon, IBM and Microsoft, have been shown to suffer from high misrecognition rates for people of color. Another commercial face-recognition technology that Amazon tried to sell to government agencies has been shown to have significantly higher error rates for minorities. And a popular selfie lens by Snapchat appears to "whiten" people's faces, apparently to make them more attractive.ADVERTISEMENTThese are not just academic curiosities.


Why are Artificial Intelligence systems biased?

#artificialintelligence

A machine-learned AI system used to assess recidivism risks in Broward County, Fla., often gave higher risk scores to African Americans than to whites, even when the latter had criminal records. The popular sentence-completion facility in Google Mail was caught assuming that an "investor" must be a male. A celebrated natural language generator called GPT, with an uncanny ability to write polished-looking essays for any prompt, produced seemingly racist and sexist completions when given prompts about minorities. Amazon found, to its consternation, that an automated AI-based hiring system it built didn't seem to like female candidates. Commercial gender-recognition systems put out by industrial heavy-weights, including Amazon, IBM and Microsoft, have been shown to suffer from high misrecognition rates for people of color.


Why are Artificial Intelligence systems biased?

#artificialintelligence

A machine-learned AI system used to assess recidivism risks in Broward County, Fla., often gave higher risk scores to African Americans than to whites, even when the latter had criminal records. The popular sentence-completion facility in Google Mail was caught assuming that an "investor" must be a male. A celebrated natural language generator called GPT, with an uncanny ability to write polished-looking essays for any prompt, produced seemingly racist and sexist completions when given prompts about minorities. Amazon found, to its consternation, that an automated AI-based hiring system it built didn't seem to like female candidates. Commercial gender-recognition systems put out by industrial heavy-weights, including Amazon, IBM and Microsoft, have been shown to suffer from high misrecognition rates for people of color.


The benefits of implementing RPA in finance

#artificialintelligence

RPA works well for simple processes that operate in relatively high transaction volumes -- and finance and accounting are ripe with them, said Craig Le Clair, vice president and principal analyst at Forrester Research. "One bank that I interviewed had 1,400 people closing the books monthly, quarterly, end of year, and they felt they could automate [the work of] about a third of those full-time employees with RPA." Imran Sabir, the senior manager of RPA at OZ, a consulting company based in Fort Lauderdale, Fla., agreed that RPA can improve an organization's end-of-year closing, which is the most hectic time for finance. The financial close and reporting process encompasses numerous tasks that involve many systems, departments and individuals, from closing out subledgers to creating and delivering financial filings to regulatory bodies, Sabir said. The process requires posting data from sources such as Microsoft Excel to these subledgers -- a tedious undertaking that RPA can mitigate and solve efficiently. Reporting is another common use case for RPA in finance, according to Sabir.


Amazon Echo may have been a witness to a suspected murder

#artificialintelligence

Police in Florida believe recordings from a murder suspect's Amazon Echo may contain crucial information as they investigate an alleged argument at the man's home that ended in his girlfriend's death. Adam Reechard Crespo, 43, is charged with murder in connection to the July death of Silvia Galva, who died after suffering a stab wound to the chest. The Broward County Sheriff's Office believes Crespo's Echo - a smart speaker that connects to the Amazon voice-activated personal assistant Alexa - may have been a witness to the crime and obtained search warrants for all the device's recordings. Hallandale Beach Police Department spokesman Sgt Pedro Abut told the Sun-Sentinel that the department has received the recordings and is "in the process of analysing the information that was sent to us". The police department did not immediately return NBC News' request for comment on Saturday.


Synechron launches AI data science accelerators for FS firms

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These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad. With this, Synechron's Global Accelerator programs now includes over 50 Accelerators for: Blockchain, AI Automation, InsurTech, RegTech, and AI Data Science and a dedicated team of over 300 employees globally.


Synechron Launches AI Data Science Accelerators for the BFSI sector

#artificialintelligence

Synechron the global financial services consulting and technology services provider, has announced the launch of its AI Data Science Accelerators for Financial Services, Banking and Insurance (BFSI) firms. These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad.