Artificial intelligence (AI) is set to transform many aspects of our lives, including our home and health. AI is already widely used in internet searches, and home devices with speech recognition, but in the near future we will see AI become even more widespread. This will have significant repercussions as AI performs many tasks that until now could only be undertaken by humans. AI will remove human intervention from much of the picture. This will particularly affect intellectual property law.
As cases of violence against women and girls have surged in South Asia in recent years, authorities have introduced harsher penalties and expanded surveillance networks, including facial recognition systems, to prevent such crimes. Police in the north Indian city of Lucknow earlier this year said they would install cameras with emotion recognition technology to spot women being harassed, while in Pakistan, police have launched a mobile safety app after a gang rape. But use of these technologies with no evidence that they help reduce crime, and with no data protection laws, has raised alarm among privacy experts and women's rights activists who say the increased surveillance can hurt women even more. "The police does not even know if this technology works," said Roop Rekha Verma, a women's rights activist in Lucknow in Uttar Pradesh state, which had the highest number of reported crimes against women in India in 2019. "Our experience with the police does not give us the confidence that they will use the technology in an effective and empathetic manner. If it is not deployed properly, it can lead to even more harassment, including from the police," she said.
As artificial intelligence (AI) becomes more pervasive and embedded in life-changing decisions, the need for transparency has intensified. There have been plenty of high-profile cases in recent years where AI has contributed to bias and discrimination, with the use of facial recognition for policing just one example. There is a high probability of a shift from loose self-regulation to government involvement in AI over the next couple of years. In turn, Big Tech is increasingly using AI to solve the privacy and bias problems that the technology itself created. Listed below are the key technology trends impacting the AI theme, as identified by GlobalData.
We have since a long time ago advanced beyond a period when propels in AI research were bound to the lab. Artificial intelligence has now become a real-world application technology and part of current life. If harnessed properly, we trust AI can convey extraordinary advantages for economies and society, and support decision-making, which is more attractive, secure and more comprehensive and educated. Yet, such promise won't be acknowledged without extraordinary consideration and effort, which incorporates regulations in AI and governance of AI. It should also focus on how its development and utilization ought to be governed, and what level of legal and moral management-- by whom, and when, is required.
The precision and promise of a data-driven society has stumbled these past years, serving up some disturbing--even damning--results: facial recognition software that can't recognize Black faces, human resource software that rejects women's job applications, talking computers that spit racist vitriol. "Those who don't learn history are doomed to repeat it," George Santayana said. But most artificial intelligence applications and data-driven tools learn history aplenty--they just don't avoid its pitfalls. Instead, though touted as a step toward the future, these systems generally learn the past in order to replicate it in the present, repeating historical failures with ruthless, and mindless, efficiency. As Joy Buolamwini says, when it comes to algorithmic decision-making, "data is destiny."
"That's part of our job, is to show people that the players on the team, even if some of them don't speak the best English and they're Korean national players, they're living here in the U.S. now. They're like you and me, they're like everybody else," Rufail said. "We're going to continue to … do a lot more content around the team to show their personality and I think people who might have a bit of a, we'll say discriminatory type personality, might understand a little bit better that our Korean players can connect with them in a way that maybe they didn't know previously."
Modern enterprises are inundated with data; however, not all data is usable as is for machine learning. Though an organization may have millions of data points, it could still have data struggles that stunt machine learning. Turning to synthetic data for machine learning can boost privacy, democratize data, minimize bias in data sets and reduce costs. More broadly, real data and synthetic data tend to be used in combination. "I can't think of any project in the AI space where you wouldn't be able to get a better outcome by leveraging synthetic data," said Kjell Carlsson, principal analyst at Forrester Research.
Over the past months, federal law enforcement has used a wide variety of surveillance technologies to track down rioters who participated in the 6 January attack on the US Capitol building – demonstrating rising surveillance across the nation. Recent news coverage of the riot has largely focused on facial recognition – and how private citizens and local law enforcement officials have conducted their own facial recognition investigations in an attempt to assist the FBI with the help of social media. But charging documents reveal that the FBI has relied on a variety of other technologies, including license plate readers, police body cameras and cellphone tracking. And civil rights watchdogs like the ACLU are concerned that the same technologies used to surveil the rioters could impede protesters exercising their first amendment rights. The Capitol riot was an exceptional event – marking the first time in centuries that insurrectionists breached the center of the US federal government.
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