Latest news with #bias
ABC News
3 days ago
- Business
- ABC News
Could AI be the 'person' deciding whether or not you get your dream job?
Have you ever been in a job interview and thought, 'this meeting could have been an interaction with an AI agent'? No? Well, two-thirds of Australian employers are now using artificial intelligence agents to conduct job interviews on their behalf. What kind of bias does a computer have when deciding on the right candidate? And could it be less objective than a human? Also, people are hacking their digital driver's licences and Grindr is once again in the spotlight for all the wrong reasons. Plus, why did the CIA secretly run a Star Wars fansite in the 2010s? GUESTS: Ariel Bogle , investigations reporter for Guardian Australia , investigations reporter for Guardian Australia Gianfranco di Giovanni, games and technology journalist for ABC Entertainment This episode of Download This Show was made on Gadigal land Technical production by Craig Tilmouth and Tim Symonds
Daily Mail
3 days ago
- General
- Daily Mail
Presiding officer accused of 'blatant bias' after throwing Tory MSP out of debating chamber
Holyrood's Presiding Officer is facing claims of 'blatant bias' after former Scottish Tory leader Douglas Ross was thrown of out the chamber without a warning. Mr Ross said it was 'absolutely' clear that former Green MSP Alison Johnstone was favouring Nationalist politicians over Unionist ones. It followed a stormy First Minister's Questions in which several Tory MSPs demanded clear answers from John Swinney on his Government's Net Zero policies. Mr Ross, who has been ticked off for heckling on previous occasions, shouted 'Deflection from Swinney again' when Mr Swinney digressed onto Brexit. The part-time football referee was immediately given the red card. 'Mr Ross, you have persistently refused to abide by our standing orders,' Ms Johnstone said. 'I ask you to leave the chamber; you are excluded for the rest of the day.' Mr Ross appeared not to grasp what was happening and had to be asked to leave again. It was the first expulsion of an MSP from the chamber in five years. A Conservative spokesman said: 'The Presiding Officer has shown a consistent pattern to favour certain parties at the expense of others. 'We will be seeking discussions to reiterate that the Presiding Officer should not show blatant bias.' Mr Ross, a Highlands & Islands MSP, later said Ms Johnstone was having a 'controlling effect' on the chamber and there was a constitutional factor involved. He said: 'You've got to look at Alison Johnstone formerly being a Green Party member, saying that she would leave her party allegiances at the door, but taking very different approaches to Nationalist politicians who step out of line compared to Unionist politicians who step out of line.' He cited her letting Mr Swinney call the Tory party 'a disgusting organisation' earlier this month, a phrase Tory leader Russell Findlay was not allowed to repeat, and added: 'Since then she's done nothing to prove to me that she is going to be neutral.' Mr Ross stopped short of demanding an apology, but said Ms Johnstone should 'reflect' and 'regret her immediate kneejerk reaction' and the lack of warning. He said: I'm looking at all the options, but the ball is in the Presiding Officer's court'. A Parliament spokeswoman said: 'The Presiding Officer has warned Mr Ross on repeated occasions recently about his behaviour in the Chamber. 'Due to his persistent refusal to respect the rules of Parliament, the Member was asked to leave the Chamber.' Mr Swinney's spokesman said the FM did not think Ms Johnstone was biased. Scottish Lib Dem leader Alex Cole-Hamilton said Mr Ross had been 'provoking' the PO for weeks, hoping to get thrown out 'in a cynical bid for relevance', adding: 'She was quite right to eject him.'
Telegraph
4 days ago
- Business
- Telegraph
Holyrood's ‘Speaker' accused of anti-Unionist bias after throwing out Tories' former leader
The Scottish Parliament's presiding officer has been accused of 'blatant bias' against opponents of independence after she ejected a former leader of the Scottish Tories from the Holyrood chamber. Alison Johnstone ordered Douglas Ross to leave the chamber during First Minister's Questions on Thursday, and banned him for the rest of the day. Speaking outside the chamber, Mr Ross said 'many people' were questioning if the presiding officer, a former MSP for the pro-independence Scottish Greens, was 'truly neutral' following a series of clashes with Tory members. He accused Ms Johnstone of 'taking very different approaches to nationalist politicians who step out of line compared to Unionist politicians who step out of line'. The Scottish Tories also claimed Ms Johnstone had 'shown a consistent pattern to favour certain parties at the expense of others' and demanded talks to deal with the alleged 'bias'. The Scottish Parliament said the presiding officer, whose job is equivalent to that of the Speaker at Westminster, had taken action against Mr Ross as he was a repeat offender and because of his 'persistent refusal to respect the rules of Parliament'. But Tory insiders alleged Ms Johnstone had reprimanded other Conservatives for conduct that she permitted among MSPs from other parties. This included barracking political opponents from their seats in the chamber. They highlighted how she repeatedly chastised Russell Findlay, the Scottish Tory leader, during First Minister's Questions on May 1 for failing to treat John Swinney with 'courtesy and respect'. However, she failed to give the First Minister a reprimand when he accused his Conservative opponent of 'barefaced dishonesty.' Ross shouted at First Minister during Questions Ms Johnstone became a Green MSP for the Lothians region in 2011, but gave up her party affiliation when she was elected presiding officer after the 2021 Holyrood election. During Thursday's First Minister's Questions, Mr Ross could be heard shouting 'deflection' at Mr Swinney as he responded to questions from Mr Findlay about the Scottish Government's net zero targets. Ms Johnstone stopped proceedings and said: 'Mr Ross, you have persistently refused to abide by our standing orders. I ask you to leave the chamber; you are excluded for the rest of the day.' Her decision to immediately expel Mr Ross appeared to surprise him and other MSPs as she had previously dealt with those who shouted from their seats by issuing a warning. After he failed to move, she ordered him out a second time and he left. Ms Johnstone also warned two other Tory MSPs, Douglas Lumsden and Stephen Kerr, about 'shouting from your seat' during the session. Mr Ross said: 'I think Alison Johnstone has to seriously consider her neutral role because at the moment from the outside many people are questioning if she is truly neutral. After what she has just done, I struggled to accept that she has been neutral for all members. 'I think we have got to look at her actions against Conservatives in general, and how she has responded to comments from SNP ministers. For example, the First Minister is apparently allowed to call the Conservatives 'a disgusting party' with no sanction.' A Scottish Tory spokesman said: 'The presiding officer has shown a consistent pattern to favour certain parties at the expense of others. We will be seeking discussions to reiterate that the presiding officer should not show blatant bias.' Mr Kerr said: 'The presiding officer should be a stout defender of the rights of MSPs to hold ministers to account. Instead, we have one who appears more concerned with shielding ministers from scrutiny than standing up for the Parliament.' 'Repeated refusals' to respect rules But a Scottish Parliament spokesman said: 'The presiding officer has warned Mr Ross on repeated occasions recently about his behaviour in the chamber. Due to his persistent refusal to respect the rules of Parliament, the member was asked to leave the chamber. ' Holyrood insiders said Ms Johnstone had warned Mr Ross about his conduct on April 3 and twice on April 22, when she asked him to 'refrain from shouting from your seat' and challenged him over whether he wanted to leave the chamber. They added that she issued a further warning on May 8. Alex Cole-Hamilton, the Scottish Liberal Democrat leader, said: 'Anyone watching First Minister's Questions will know that for weeks and week Douglas Ross has been provoking the presiding officer in the hope that he would get thrown out, in a cynical bid for relevance. 'The presiding officer was quite right to eject him and my party has full confidence in her impartiality.'
Geeky Gadgets
6 days ago
- Geeky Gadgets
The Dark Side of Generative AI : 10 Problems We Can't Ignore
What happens when a technology designed to transform creativity and innovation also threatens to unravel trust, fairness, and even the environment? Generative AI, hailed as a new force in industries from entertainment to healthcare, comes with a host of challenges that are impossible to ignore. From creating eerily convincing deepfakes to amplifying biases embedded in its training data, this technology is as controversial as it is fantastic. The stakes are high: while generative AI has the potential to reshape the future, its unchecked growth could lead to profound ethical dilemmas, societal disruptions, and environmental consequences. Are we prepared to confront the darker side of this innovation? The AI Grid team explores the 10 most pressing problems with generative AI, offering a critical lens on its technical, ethical, and societal implications. You'll uncover how biases in training data can perpetuate inequality, why the rise of AI-generated misinformation threatens public trust, and how the technology's energy demands are creating a hidden environmental toll. But these challenges are not insurmountable. By understanding the risks, we can begin to ask the right questions and demand solutions that prioritize transparency, fairness, and sustainability. As we navigate this uncharted territory, the question isn't just what generative AI can do—but at what cost? Generative AI Challenges Bias and Fairness Generative AI systems often reflect the biases present in their training data, which can lead to outputs that unintentionally reinforce stereotypes or discriminatory patterns. For instance, an AI trained on biased hiring data may favor certain demographics, perpetuating inequality in recruitment processes. This issue underscores the importance of using diverse and representative datasets during training. Additionally, implementing rigorous fairness testing and bias detection mechanisms is crucial to ensure equitable outcomes. By addressing these concerns, developers can create systems that promote inclusivity and fairness across various applications. Misinformation Risks The ability of generative AI to produce highly convincing fake content poses a significant threat to the integrity of information. Examples such as deepfake videos or fabricated news articles demonstrate how this technology can blur the line between reality and fiction. The misuse of AI-generated content can erode public trust and accelerate the spread of false information. To combat this, it is essential to develop robust verification mechanisms and tools that can distinguish between authentic and AI-generated content. These measures are critical for maintaining credibility and making sure the responsible use of generative AI in the digital age. 10 BIG Problems With Generative AI Watch this video on YouTube. Below are more guides on Generative AI challenges from our extensive range of articles. Intellectual Property Challenges Generative AI raises complex questions about intellectual property (IP) rights, particularly when it creates content inspired by existing works. For example, if an AI generates artwork influenced by a copyrighted painting, determining ownership becomes a legal gray area. This uncertainty is especially significant in creative industries, where originality and ownership are central to success. Addressing these challenges requires clear legal frameworks and policies that balance innovation with the protection of intellectual property. Establishing guidelines for attribution and ownership will be key to resolving disputes and fostering trust in AI-generated content. Ethical Implications The ethical challenges associated with generative AI are vast and multifaceted. On one hand, you might question whether relying on AI-generated content diminishes the value of human creativity. On the other hand, there are concerns about the potential misuse of this technology for malicious purposes, such as creating harmful propaganda or manipulating public opinion. For instance, generative AI could be weaponized to spread disinformation or incite social unrest. To address these risks, it is essential to establish comprehensive ethical guidelines that govern the development and use of generative AI. These guidelines should prioritize transparency, accountability, and the promotion of societal benefits. Environmental Impact The environmental impact of generative AI is a growing concern, as training and operating large models require immense computational power. This translates to significant energy consumption and carbon emissions. For example, training a single advanced AI model can emit as much carbon as several cars over their lifetimes. To mitigate this impact, developers must focus on optimizing algorithms to reduce energy requirements and adopt renewable energy sources for powering data centers. By prioritizing sustainability, the AI community can minimize the environmental footprint of generative AI while continuing to innovate. Lack of Transparency Generative AI models often function as 'black boxes,' making it difficult to understand how they produce specific outputs. This lack of transparency can undermine trust, especially in high-stakes applications such as healthcare, finance, or legal decision-making. For example, if an AI system recommends a medical treatment without explaining its reasoning, it can create uncertainty and hesitation among users. Developing explainable AI tools is essential to enhance accountability and ensure that users can trust the technology's decisions. Transparent systems will also enable better oversight and regulation, fostering confidence in AI-driven solutions. Dependence on Data Quality The performance of generative AI is heavily dependent on the quality and diversity of its training data. If the data is biased, incomplete, or outdated, the AI's outputs will reflect those flaws. For instance, an AI trained on outdated medical records might generate inaccurate diagnoses, potentially endangering patients. Making sure robust data curation and validation processes is critical to improving the reliability and accuracy of generative AI systems. By prioritizing high-quality data, developers can create models that deliver consistent and trustworthy results across various applications. Job Displacement Generative AI is transforming the workforce, particularly in creative fields such as content writing, graphic design, and music composition. While this technology can enhance productivity and efficiency, it also poses a risk to jobs that rely on human creativity and expertise. For example, AI-generated content may reduce the demand for traditional copywriting or design roles. Preparing for this shift requires reskilling and upskilling initiatives to help workers adapt to new roles that use human-AI collaboration. By embracing these changes, industries can create opportunities for innovation while minimizing the negative impact on employment. Security Risks Generative AI can be exploited for malicious purposes, such as creating deepfakes, automating cyberattacks, or generating convincing phishing emails. These threats pose significant challenges for cybersecurity professionals, as AI-generated content can be difficult to detect and counteract. Strengthening security systems and monitoring mechanisms is critical to mitigating these risks. Additionally, fostering collaboration between AI developers and cybersecurity experts can help identify vulnerabilities and develop proactive solutions to prevent misuse. Regulatory Uncertainty The rapid advancement of generative AI has outpaced the development of laws and regulations to govern its use. This creates uncertainty for stakeholders navigating unclear legal landscapes, particularly regarding liability, accountability, and ethical considerations. Policymakers must collaborate with technologists, industry leaders, and ethicists to establish clear and enforceable guidelines that balance innovation with societal protections. By addressing regulatory gaps, governments can ensure that generative AI is developed and deployed responsibly, fostering trust and stability in its applications. Media Credit: TheAIGRID Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Yahoo
7 days ago
- Politics
- Yahoo
Survey: Most Americans say Asians face discrimination in US
[Source] A majority of Americans say Asian people face at least some discrimination in the U.S., according to a new Pew Research Center survey released Tuesday. Asian American respondents were even more likely to perceive bias, with over four in five reporting some or a lot of discrimination against their group. The nationally representative survey of 3,589 U.S. adults, conducted in April, found that 66% of Americans believe Asians face discrimination. Among Asian American respondents, 82% said their group encounters either some or a lot of discrimination — a higher proportion than reported by Black (69%), Hispanic (66%) or White (64%) respondents about their own groups. Partisan divide on discrimination Perceptions of anti-Asian discrimination vary sharply by political affiliation. Among Democrats and Democratic-leaning independents, 83% said Asian people face at least some discrimination — a figure that has remained largely unchanged since 2024. Trending on NextShark: The share of Republicans and Republican-leaning independents who say Asian people face discrimination dropped from 66% in 2024 to 51% in 2025 — a notable year-over-year decline. Across both parties, Asian respondents were more likely than their White or Hispanic peers to say Asian people face discrimination. How other groups compare Trending on NextShark: When asked about other groups, 74% of Americans said Black people face at least some discrimination, followed by 72% for Hispanic people and 77% for transgender individuals. By contrast, fewer than half — 45% — said White people face discrimination. The survey also found that younger adults were more likely than older Americans to perceive discrimination against Asian people. Among those ages 18 to 29, 77% said Asians face at least some discrimination, compared to 57% of adults 65 and older. This generational gap mirrors broader trends in perceptions of race and inequality, suggesting that younger Americans may be more attuned to issues of racial bias. This story is part of The Rebel Yellow Newsletter — a bold weekly newsletter from the creators of NextShark, reclaiming our stories and celebrating Asian American voices. Trending on NextShark: Subscribe free to join the movement. If you love what we're building, consider becoming a paid member — your support helps us grow our team, investigate impactful stories, and uplift our community. Subscribe here now! Trending on NextShark: Download the NextShark App: Want to keep up to date on Asian American News? Download the NextShark App today!
