Technology And Society

For years, companies have ramped up the surveillance of their employees, often without the employees' knowledge (or consent): monitoring what websites they accessed, their phone calls, their keystrokes, the speed with which they drove, and numerous other things. As this piece points out, the advent of AI makes actually analyzing all the tracking data much easier and less expensive, so the surveillance industry is about to get even bigger. In the U.S., when employees go to work, they give up their rights--to speak, to privacy, to criticize their companies and their decisions, and so many other things. As this piece also notes and as social science research has frequently demonstrated, job autonomy is an important determinant of motivation. With people under ever more monitoring, it is little wonder that job satisfaction and engagement, and for that matter trust in organizations, has fallen. When will companies learn to value human intelligence and motivation as much as they value the artificial variety? #motivation #surveillance #bossware #management #leadership #jobautonomy

For 60 Years, Kids’ TV Cast Boys As ‘Doers’ And Girls As Passive, Study Suggests: “New research reveals that the language in children’s television is reinforcing harmful gender stereotypes, and that little has improved in 60 years. In some cases, the gender bias is getting worse over time. The study, published this week in Psychological Science, examined scripts from 98 children’s television programs in the U.S. spanning from 1960 to 2018. The researchers employed natural language processing tools to examine which words were more likely to be associated with male characters and which were more likely to be associated with female characters. In total, they analyzed 6,600 episodes, 2.7 million sentences and 16 million words. Among the shows studied were classics like The Flintstones (1960) and more modern series like The Powerpuff Girls (2016) and Lost in Space (2018). In particular, the researchers examined how often male and female characters were portrayed as active agents (those who do) versus passive recipients (those who are done to). They found that boys are ‘doers’ while girls are the ‘done-tos.’ Perhaps most shockingly, when the researchers examined how this language has changed over time, they found that it hadn’t. The gender gap in who takes action in these programs hasn’t improved in six decades. Given the amount of time children spend watching television, the study authors suggest that those who watch these programs will develop biased ideas about how women and men behave in the real world. ‘These biases aren’t just about who gets more lines; they’re about who gets to act, lead, and shape the story. Over time, such patterns can quietly teach children that agency belongs more naturally to boys than to girls, even when no one intends that message,’ professor of psychology at NYU and an author on the paper Andrei Cimpian explained in a press release. AI learning models that train on program scripts pose an additional threat of perpetuating the gender bias. The study authors explain in their paper, “The rising popularity of script-writing programs powered by artificial intelligence (AI), which are trained on language from pre-existing screenplays, adds urgency to the goal of uncovering social biases in the language in children’s media.’ As technology continues to evolve, it becomes increasingly important to understand the messages we’re sending.” Read more 👉 https://lnkd.in/e5g6Z8WF ✍️ Article by Kim Elsesser #WomenInSTEM #GirlsInSTEM #STEMGems #GiveGirlsRoleModels

California's latest regulatory move offers a clear signal for enterprise HR, yet many leaders are overlooking it simply because the initial target is the gig economy. Last week, the California Privacy Protection Agency (CalPrivacy) launched its first formal sectoral audit. The focus is gig platforms. The objective is determining whether these organisations actually allow workers to access the data shaping their livelihoods. This introduces a pragmatic reality for people management: algorithmic due process. For years, workforce data collection has operated on a model of strict 'Data Asymmetry'. Employers hold the raw datasets, which include behavioural metrics, performance scoring and communication logs. The worker simply receives the final output. That output could be a shift allocation or an automated termination flag. California is directly challenging this asymmetry. Under state privacy laws, workers possess a legal right to understand the precise personal data an algorithm processes to reach decisions about their employment. Logically, a worker cannot contest a machine-generated decision without seeing the underlying inputs. If you oversee HR technology, people analytics or talent acquisition, it is worth viewing this as an early indicator of enterprise regulation. Legal frameworks frequently test compliance at the edges of the workforce before moving into the corporate centre. Consider the data your current HR infrastructure actively collects today: • Productivity monitoring outputs • AI-driven interview assessments • Flight-risk prediction scores If an employee asks to see the raw data informing an AI-generated "low potential" flag, your systems should theoretically be able to isolate and provide it. The transition from black-box algorithms to the Glass Box Mandate is shifting from an abstract debate to an active compliance requirement. You can certainly continue deploying advanced HR analytics to drive efficiency, but you must govern the data access risks properly. Review your vendors this quarter to understand how they support employee data access requests for algorithmic decisions. Preparing for data symmetry now builds operational resilience for whatever regulatory framework arrives next.

As AI tools advance rapidly, it's important for employers to understand where the ethical and legal boundaries lie. The EU AI Act has taken a firm stance: AI systems that infer personality or emotions from biometric data — including face-based personality prediction — are prohibited or classified as high-risk. The legislation recognises the profound risks these tools pose to fairness, discrimination, privacy, and human dignity. In Australia, no equivalent protections currently exist. This means technologies that would be unlawful in Europe could still enter the Australian recruitment market — without the guardrails needed to prevent discrimination or algorithmic bias. As employers explore AI for hiring, screening, or talent management, now is the time to stay alert: —Be cautious of AI tools claiming to “predict personality” or “assess fit” from images or videos. —Demand transparency, validation evidence and bias testing from vendors. —Ensure any AI used in HR aligns with ethical standards — even if legislation lags behind. Until stronger regulation arrives in Australia, the responsibility rests with employers to safeguard their people and their processes from high-risk AI. Join the growing community of multidisciplinary leaders for inclusive and ethical AI at ada.ai.

Imagine this ⬇ . . . . You're applying for a job, and an AI sifts through every social media post, every digital breadcrumb you've left online, extracting a psychological profile that can make or break your application. It's not science fiction – it's happening now. Some AI technologies claim to assess talent by analysing candidates' online behaviour, inferring traits like personality, emotional stability, and "cultural fit." But this trend raises profound ethical questions: Privacy Invasion: Should your tweets or Facebook posts be fair game for hiring decisions? Do you have the right to digital anonymity? Bias and Discrimination: Algorithms can encode and amplify societal prejudices. Will certain demographics be unfairly filtered out? Accuracy and Fairness: How reliably can AI interpret context, satire, or evolving identities across digital platforms? Transparency and Consent: Are candidates informed about the AI assessments being conducted, and can they challenge or review the results? While AI has the potential to revolutionise talent matching, we must establish robust safeguards, regulations, and ethical standards. Human lives and careers deserve more than a silent, unseen algorithm making pivotal decisions. As we move towards an AI-driven hiring era, we must ask ourselves: Do we want efficiency at the cost of ethics? #EthicsInAI #Hiring #Privacy #ArtificialIntelligence #FutureOfWork

🚀 𝐄𝐌𝐏𝐋𝐎𝐘𝐌𝐄𝐍𝐓 𝐀𝐍𝐃 𝐀𝐈: 𝐍𝐄𝐖 𝐀𝐔𝐒𝐓𝐑𝐀𝐋𝐈𝐀𝐍 𝐑𝐄𝐆𝐔𝐋𝐀𝐓𝐎𝐑𝐘 𝐑𝐄𝐏𝐎𝐑𝐓🚀 AI is already reshaping our lives. One of the most profound transformations is happening in the workplace. AI is changing how we do our jobs—and soon, it will change which jobs exist at all. Some roles will disappear, while new ones emerge. Naturally, unions are concerned—not just about job losses, but about mental health, workplace safety, and the risks of unregulated AI adoption. They have been vocal in demanding that workers be at the centre of AI adoption decisions. We are at a crossroads: how do we balance AI-driven productivity gains with the impact on workers? 📢 The House Standing Committee on Employment, Education and Training has released a report on the digital transformation of workplaces, examining the rapid rise of automated decision-making and machine learning in employment. 107 pages of insights, challenges, and, crucially, 21 recommendations. There's a lot in there, but some key details include: 📌 Regulating AI in employment – The report recommends that AI used in employment decisions (such as hiring and termination) be classified as high-risk, ensuring stronger oversight and safeguards against unfair or biased outcomes. 📌 Strengthening worker privacy protections – It's clear the current privacy laws fail to protect workers’ privacy. At the same time, the Fair Work Act does not contain dedicated privacy protections. The report recommends: 🔹 Banning high-risk uses of workers data, such as providing it to AI developers. 🔹 Prohibiting the sale to third parties of workers’ personal data. 🔹 Requiring transparency in workplace surveillance and data use. 🔹 Empowering the Fair Work Commission to handle privacy-related complaints. 📌 Ensuring worker consultation on AI adoption – Employers should be obligated to consult workers throughout AI adoption, ensuring that new technologies are implemented fairly and do not unfairly disadvantage employees. 📌 Mandating independent AI audits – Government audits of AI are recommended to monitor bias, fairness, and compliance, ensuring AI decisions meet ethical and legal standards. The industrial relations fire has long been burning between unions, employees, and employers—and AI is accelerant. We must strike the balance between AI adoption and worker protections. The employee records exemption leaves many workers without real privacy protections. If AI is to be used fairly in workplaces, reforms here will be just as important as AI-specific regulation. It's inevitable that many workers will be impacted by the AI revolution, but get policies right—and Australia wins. Support AI-driven innovation while ensuring retraining, transparency, and fairness. Get it wrong—and we risk exacerbating job insecurity, discrimination, and workplace inequality - we all lose. #AI #FutureOfWork #Privacy #CyberSecurity #ArtificialIntelligence #EmploymentLaw #DigitalTransformation #AIRegulation

Candidates should be genuinely concerned about how companies use AI-powered Applicant Tracking Systems (ATS) and sourcing tools. TA Tech companies also have a real opportunity to continue to improve and differentiate. Here's why ↴ 1. Fairness and Bias → Concern: AI systems may perpetuate or even amplify biases if the training data is not diverse or if the algorithms are not rigorously tested. → Candidate Worry: Will the AI unfairly disqualify me based on factors like my name, background, or employment history? 2. Transparency → Concern: Candidates often don’t know how AI evaluates their resumes or application responses. → Candidate Worry: How are decisions being made, and what criteria are used? If I’m rejected, will I even know why? 3. Loss of Human Touch → Concern: Over-reliance on AI may result in less personal interaction during the hiring process, which requires empathy and context. → Candidate Worry: Am I being overlooked because a machine doesn’t see my unique skills or context that a human recruiter might appreciate? 4. Accuracy of Matching → Concern: AI might prioritize keyword matching over context or nuance in a candidate’s experience. → Candidate Worry: Will the system recognize my transferable skills, or is it just searching for buzzwords? 5. Data Privacy → Concern: AI tools often process large amounts of candidate data, raising privacy and security issues. → Candidate Worry: How is my personal information being stored, shared, or used? 6. Over-automation → Concern: If AI is used too heavily in sourcing and screening, good candidates may slip through the cracks. → Candidate Worry: Am I being filtered out by rigid algorithms before anyone even looks at my application? 7. Algorithmic Accountability → Concern: Candidates want assurance that AI errors can be identified and corrected. → Candidate Worry: If the AI makes a mistake about my application, who’s accountable, and can it be reversed? How would I even know? How Companies and Vendors Can Address These Concerns ↴ →Self-audit their AI tools regularly for bias and fairness. → Provide transparency by clearly communicating how AI impacts the hiring process. → Use AI to assist, not replace, human decision-making. → Ensure data privacy through compliance with laws like GDPR or CCPA. 👆 These efforts can help build trust with candidates while ensuring that AI remains a tool to enhance, not diminish, the recruitment process. ✅ Candidates: Did I miss anything? ✅Companies: There is a massive opportunity to listen to job seekers and internal TA teams in the trenches as you develop the next phase of AI-powered TA tools. Exciting times, people! And I am here for all of it!

Microsoft’s Majorana 1 reignited the buzz about our quantum future. Here’s why Quantum is an important step forward for the world: Traditional computers struggle with solving some problems that quantum computing can easily tackle. When it comes to drug discovery, for example, traditional computers must approximate solutions for molecular behavior, often at the expense of time and precision. Quantum computing, leveraging the unique properties of quantum mechanics, promises to simulate these interactions with far greater accuracy and efficiency. This means accelerating the discovery of new drugs and potentially revolutionizing healthcare. Just as AI has sped up our ability to innovate, pairing it with quantum computing could supercharge that acceleration. Unlike AI, Quantum won’t be something that hits consumers with a “Chat GPT moment” right now. The impact of quantum breakthroughs will be felt in improved healthcare, better materials, and smarter technologies that enhance our daily lives in the background. It’s also important to note: Majorana 1 and other breakthroughs are a massive step forward in building a quantum-world, but history reminds us that transformative change is often a journey. Even the loudest proponents agree—real, tangible benefits won't happen instantly. Yet, as with every pioneering technology, the potential is immense, and the iterative process of innovation will get us there.

Imagine a technology that could radically transform how we compute, solve complex problems, and address global challenges. This is the promise of quantum computing. A striking example of its potential is transforming the fertilizer production industry, which significantly impacts global electricity consumption and greenhouse gas emissions, accounting for about 1% of the world's electricity use. Quantum computing, based on quantum mechanics principles, introduces systems capable of existing in multiple states simultaneously, dramatically speeding up complex computations. This revolutionary technology can redefine AI, cybersecurity, and research and development while tackling critical global issues like climate change. The emergence of quantum computing necessitates new programming languages, development tools, and data processing techniques. Quantum computing is crucial in designing energy storages for renewable energy systems supporting initiatives like the International Solar Alliance. By improving the efficiency of these systems, quantum computing aligns with global clean energy goals, aiding in the transition to sustainable energy sources. The impact of quantum computing on AI is profound. It promises new, interdisciplinary innovations, redefining problem-solving and technological development. Its ability to simulate complex systems, from molecular structures to environmental systems, is fascinating, enabling AI to predict the behaviour of molecules to the dynamics of ecosystems. In security, quantum computing presents both challenges and opportunities. It could render current cryptography systems obsolete, prompting concerns in digital security. Simultaneously, it's spurring the development of quantum-resistant algorithms, a key focus for entities prioritizing security, including national governments. In R&D, particularly in simulating complex physical and chemical processes quantum can be a game changer. This can significantly reduce the time and costs associated with innovation, leading to rapid advancements in pharmaceuticals, materials engineering, and environmental science. We must prioritize education and training in quantum computing principles and applications as we navigate this quantum leap. This is essential to ensure equitable access to quantum technology and avoid deepening global inequalities or Quantum colonization. As governments worldwide recognize the transformative potential of quantum technologies, they are formulating policies to guide their ethical development and use. These initiatives, aiming to foster research, promote industry collaboration, and build necessary quantum infrastructure, ensure that quantum advancements are secure, responsible, and beneficial for society. #BigIdeas2024 Note: I generated the Image using DALL-E

As quantum computing progresses, its vast potential can only be fully realized if ethical and inclusive governance prevents disparities in access and control, ensuring that security, equity, and transparency guide its development rather than allowing it to widen existing technological and social divides. Quantum computing is set to transform fields like cryptography, materials science, and optimization, but its governance must address key challenges. Sustainability is vital due to the high energy demands of quantum processors, while equitable access ensures that breakthroughs benefit society rather than a select few. Cybersecurity plays a crucial role, as quantum computers could break current encryption methods, requiring new cryptographic standards. Workforce development is essential to train professionals capable of handling this complex technology, and open innovation fosters collaboration across industries and nations. Without clear principles, quantum computing risks becoming an exclusive tool for dominant entities rather than a driver of global progress. #QuantumComputing #Cybersecurity #AIethics #TechGovernance #DigitalTransformation