Building Trust In Software

AI in healthcare isn’t as neutral as you think.  AI could harm the very patients it’s meant to help. Without addressing the bias, we will never be able to benefit from the good. Here’s how we can fix it.  1. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 AI models are only as good as the data they are trained on. Unfortunately, many datasets lack diversity, often overrepresenting patients from certain regions or demographics. Ensuring datasets are inclusive of all populations is key to reducing bias. 2. 𝗥𝗶𝗴𝗼𝗿𝗼𝘂𝘀 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 AI tools must be tested across diverse populations before deployment. Studies have highlighted how biased algorithms can worsen health disparities at every stage of development. Rigorous validation ensures that these tools perform equitably for all patients. 3. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Healthcare professionals need to understand how AI models make decisions. Lack of transparency can lead to mistrust and misuse. Explainable AI not only builds trust but also helps identify and correct biases in the system. 4. 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 Bias mitigation requires collaboration between AI developers, clinicians, policy makers, and patient advocates. Diverse perspectives help identify blind spots and create solutions that work for everyone. 5. 𝗢𝗻𝗴𝗼𝗶𝗻𝗴 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Bias doesn’t stop at deployment. Continuous monitoring is needed to ensure AI tools adapt to new data and evolving healthcare needs. For instance, algorithms trained on outdated or incomplete data may maintain errors over time. Only by addressing these areas, can we see the benefits of AI in healthcare, such as reducing errors, aiding diagnoses, and personalizing treatments for all. What steps are your organization taking to ensure fairness in AI healthcare tools?

The Hidden Risk: How Trusting the Wrong MSP Almost Crumbled a Financial Firm A quiet financial office in the suburbs thought they were secure. With IT outsourced to a "trusted" Managed Service Provider (MSP), leadership rested easy—until a routine risk assessment revealed glaring vulnerabilities that shattered their confidence. The Shocking Discovery The MSP hadn’t updated the server in over two years. Critical failures included: ➙ No vulnerability scans to uncover risks. ➙ Zero incident response plans—leaving the firm unprepared for a breach. The Risk: Wide-Open Exposure Sensitive client records, personal data, and banking details were sitting ducks for: ↳ Data breaches and ransomware. ↳ Financial fraud and identity theft. ↳ Reputational damage that could destroy trust—and the business. In the financial industry, trust is non-negotiable. A single breach can be catastrophic. Lessons Learned: Vet Your MSP Thoroughly Outsourcing IT is not outsourcing responsibility. To safeguard your business, ask these critical questions: ➙ Do they patch vulnerabilities regularly? ➙ Are there cybersecurity policies for encryption, MFA, and password management? ➙ Do they test incident response plans? ➙ Are they compliant with SOC 2, ISO 27001, or industry standards? ➙ How do they assess their vendors for third-party risks? The Power of Proactive Risk Assessments This firm learned that blind trust isn’t a strategy. A single assessment empowered them to: ➙ Replace their MSP with one prioritizing robust security. ➙ Implement stronger internal policies to safeguard data. ➙ Protect their business and rebuild client trust. PS: When’s the last time you evaluated your MSP? Is your data really secure? ♻️ Repost to raise awareness about third-party risks. 🔔 Follow Brent Gallo - CISSP for actionable insights to secure your business. #CyberSecurity #ThirdPartyRisk #MSPFailure #DataProtection #FinancialIndustry #RiskManagement #ITSecurity

What Global OEMs Expect from 𝗜𝗻𝗱𝗶𝗮𝗻 𝗥𝗲𝗰𝘆𝗰𝗹𝗲𝗿𝘀. When we first got a call from a global OEM, it wasn’t just a business opportunity for us. It was a 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝘀𝗵𝗶𝗳𝘁. Because global brands don’t just hand over their waste. They hand over their reputation. Working with Top Brands taught me that it’s not about just being “good enough” to handle batteries or e-waste. It’s about being thorough, clear, and consistently dependable. They ask tough questions. They care about how material is tracked and what you’re doing with every gram of recovered content. And more importantly, they want to know 𝘄𝗵𝘆 you do things the way you do. That is where many Indian recyclers lose the plot. Because this isn’t about just ticking compliance boxes. This is about building systems that are 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁. The brands choose to work with us because we show up with clarity, we admit when something needs fixing, and we keep our processes transparent. In a space as sensitive as battery and e-waste recycling, trust is not built with big promises. It’s built with small, consistent actions. - Every email was responded to on time. - Every commitment is honoured. - Every report is ready before the ask. That’s what global partnerships need. And frankly, that’s what the future of Indian recycling depends on. If you’re building a recycling or waste-management business, what are you learning from global expectations?

The Infrastructure of Trust by S. Chung, PhD — Dialogue between Board Chair, Executive Director, and Indigenous Director Executive Director: We’ve built systems—policies, reports, KPIs—yet it still feels like control, not coherence. Why? Board Chair: Fear of failure builds reports. Insecurity builds hierarchy. Few build what truly holds everything together: trust. Indigenous Advisor: And trust is not policy. It’s breath between people. You can’t measure it—but you can feel it when it breaks. Executive Director: Trust sounds like…so soft. Board Chair: It isn’t. It’s structure. When trust holds, people risk truth. When it cracks, strategy dies in polite silence. Indigenous Advisor: In our ways, trust is ceremony—rebuilt through listening until words and actions walk together. Actions for Trust and Listening 1. Budget for Listening — Create pay systems for Elders, meals, and circles. 2. Listening Minutes — End meetings with one truth heard, one not. 3. Relational Reporting — Replace one KPI with a story of restored relationship. 4. Reverse Consultations — Ask partners to grade how we listen. 5. Land-Based Orientation — Begin projects with a land walk. 6. Trust Metrics — Track how fast truth moves from voice to action. 7. Courage Reviews — Ask: When did you tell an uncomfortable truth this year? 8. Silence Logs — The issues avoided are your data. Executive Director: So trust becomes the system? Indigenous Advisor: Yes. When listening changes the plan, you’ve begun to decolonize. With gratitude to Ktunaxa land, colleagues, and to all allies who work through relationship and listening.

EDPS - European Data Protection Supervisor: Human #Oversight of #ADM Automated Decision-Making Two centuries of technological progress have redefined society, as industrial and digital automation have modified how people live, work, and how we interact with each other. A significant development in this evolution has been the automation of decision-making processes with the development of systems that not only execute tasks but also make decisions that can affect individuals’ lives and rights. While technological advances in ADM offer significant potential, they also introduce risks of opacity, bias, and discrimination in decision outcomes. Such risks can undermine trust in technology and lead to violations of individual rights, as well as broader harm to democratic processes and societal cohesion. Individuals might not always be aware that they are subjected to ADM. This can create an imbalance of power between those affected by these systems and those who design, deploy, or control them. As ADM becomes increasingly integrated into processes, tools, and services, it is essential to ensure that these systems are not left to make autonomous, uncontrolled decisions that affect individuals' fundamental rights. Therefore, the involvement of humans as a safeguard against the risks associated with ADM systems (e.g., algorithmic bias and misclassification) is increasingly perceived as necessary. Integrating human judgment at various stages - during the design, real-time monitoring, or post-decision audits - can help ensure that ADM systems align with ethical standards, societal values, and regulations. However, simply adding a human within the decision-making process does not inherently ensure better outcomes, nor should it serve as a means to deflect accountability for the system’s decisions. In fact, just including a human is unlikely to prevent systems from producing wrongful or harmful outcomes for individuals. This is frequently due to inadequate implementations or lack of control over the system - issues that will be further examined in this document. The objective of this TechDispatch is twofold. First, it examines common assumptions about how humans interact with and monitor decision-making systems, highlighting the overly optimistic nature of many of these assumptions. Second, it explores practical measures that providers and deployers of ADM systems can take to ensure that human oversight supports democratic values and safeguards human rights. It is important to note that this Tech Dispatch does not aim to offer any legal interpretation. Instead, it focuses on exploring how the implementation of human oversight can impact its overall effectiveness. This TechDispatch builds upon the knowledge gathered during the Internet Privacy Engineering Network (IPEN) event, organised by the EDPS and the Karlstad University in September 2024.

I walked into a room full of frustration. The project was off track, the budget was bleeding, and trust had worn thin. As the new project manager, I had 30 days to rebuild what was broken not just the plan, but the relationships. 💡 Here’s the exact trust-building strategy I used to shift the momentum one conversation, one quick win, and one honest update at a time. ▶ Day 1–5: I started with ears, not answers. 🎧 Active Listening & Empathy Sessions I sat down with stakeholders one by one, department by department. No slides. No status updates. Just questions, empathy, and silence when needed. 💬 I didn’t try to fix anything. I just listened and documented everything they shared. Why it worked: They finally felt heard. That alone opened more doors than any roadmap ever could. ▶ Day 6–10: I called out the elephant in the room. 🔍 Honest Assessment & Transparent Communication I reviewed everything timelines, budgets, blockers, and team dynamics. By day 10, I sent out a clear, no-spin summary of the real issues we were facing. Why it worked: I didn’t sugarcoat it but I didn’t dwell in blame either. Clarity brought calm. Transparency brought trust. ▶ Day 11–15: I delivered results fast. ⚡ Quick Wins & Early Action We fixed a minor automation glitch that had frustrated a key stakeholder for months. It wasn’t massive, but it mattered. Why it worked: One small win → renewed hope → stakeholders leaning in again. ▶ Day 16–20: I gave them a rhythm. 📢 Clear Communication Channels & Cadence We set up weekly pulse updates, real-time dashboards, and clear points of contact. No more guessing who’s doing what, or when. Why it worked: Consistency replaced confusion. The team knew what to expect and when. ▶ Day 21–25: I invited them to the table. 🤝 Collaborative Problem-Solving Instead of pushing fixes, I hosted solution workshops. We mapped risks, brainstormed priorities, and made decisions together. Why it worked: Involvement turned critics into co-owners. People support what they help build. ▶ Day 26–30: I grounded us in reality. 📅 Realistic Expectations & Clear Next Steps No overpromising. I laid out a realistic path forward timelines, budgets, trade-offs, and all. I closed the month by outlining what we’d tackle next together. Why it worked: Honesty created stability. A shared plan gave them control. 💬 In 30 days, we hadn’t fixed everything but we had built something more valuable: trust. And from trust, everything else became possible. Follow Shraddha Sahu for more insights

🛑 The Hidden Cost of AI Bias: A Call to Action🛑 Artificial intelligence’s transformation of industries is not without pitfalls. Bias in AI systems, whether in data or decision making, can quietly undermine trust, spark reputational crises, and even derail strategic goals. These issues aren’t just ethical, they’re also business critical. Bias in AI, though highly likely, isn’t completely inevitable. It can be identified, managed, and even prevented, to a degree, with the right tools. By anchoring your AI governance in #ISO42001 (#AIMS) and leveraging complementary standards like ISO12791, 24027, and others, your organizations can move beyond reactionary fixes to build systems that are inherently fair and resilient. ➡️ Bias in AI: What’s at Stake? Unchecked bias manifests in several ways: 1️⃣ Erosion of Trust: When algorithms treat individuals unfairly—say, favoring certain groups in hiring or lending—it damages public perception and confidence in AI systems. 2️⃣ Financial Risks: Bias can lead to lawsuits or regulatory fines, especially as global AI regulations grow more nebulous and austere. 3️⃣ Missed Opportunities: A biased AI system delivers flawed results, hindering innovation and progress. Addressing bias must focus on ensuring AI systems deliver the value they promise while minimizing harm. ➡️ Standards: A Roadmap for Tackling Bias Governance frameworks like ISO42001 establish the foundation for governing and managing AI systems, emphasizing accountability and transparency. Complementing this core are specialized standards that address bias head-on: #ISO12791: Focuses on identifying and correcting bias in machine learning (ML) models by assessing data representativeness, defining fairness metrics, and embedding checks for unintended skew in outputs. 🔸 Takeaway: Helps organizations evaluate the root of bias (data and algorithms) and implement mitigation strategies. #ISO24027: Addresses bias in decision making processes, emphasizing fairness audits and accountability across AI lifecycle stages. 🔸Takeaway: Ensures that even as decisions scale, fairness and ethical considerations remain central. #ISO5338: Guides lifecycle management to continuously assess and adjust systems, recognizing that bias risks evolve as AI systems adapt and grow. 🔸Takeaway: Establishes an ongoing process for bias detection and correction, ensuring long-term fairness. #ISO5339: Helps map stakeholder needs to ensure diverse perspectives are accounted for during design and deployment. 🔸Takeaway: By focusing on inclusivity, this standard ensures AI systems reflect the needs of all stakeholders in your ecosystem. #ISO42005: Provides a framework for AI impact assessments, focusing on evaluating societal, ethical, and operational consequences. 🔸Takeaway: Proactively identifies potential bias impacts before they result in harm.

Digital Sovereignty Begins with Identity – Trump’s Order to Disrupt the ICC’s Work and Why Europe Must Act Now Last week, Microsoft blocked the email account of the International Criminal Court (ICC) chief prosecutor – following U.S. government sanctions. No security breach. No technical fault. It was a political decision, enforced by a U.S. tech giant (Microsoft) on behalf of U.S. foreign policy. This is not an isolated event. #Digital #Identity is the Core of Digital Sovereignty Whoever controls digital identity controls access to communication, cloud services, supply chains, and contracts. That includes individuals, companies, and public authorities. Without sovereign digital identities, Europe remains dependent—on foreign infrastructure, foreign laws, and foreign interests. The ICC incident proves: U.S. big tech can bring not only global justice to a halt, but also Europe’s industrial core. Tomorrow it could be your regulator. Your company. Your infrastructure. #Europe’s #Answer: Identity Wallets for All The European Digital Identity Wallet (EUDI Wallet) and the European Business Wallet (EUBW) mark a strategic breakthrough: 👉 Natural persons (citizens and residents without citizenship) 👉 Legal persons (enterprises, associations, governments) 👉 Digital agents (AI, machines, IoT systems) All can hold verifiable credentials, sign documents, authenticate securely, and delegate trust – within a European trust infrastructure. No more blind trust in U.S. app stores, login buttons, or identity APIs. Why This Is a #Game #Changer 1️⃣ Economic Impact: The EUBW enables fast, secure onboarding, KYC/KYS, supply chain transparency, and automated compliance across industries. 2️⃣ Cybersecurity: Brings Zero Trust to B2B and B2G interactions, based on verifiable identities—not IP addresses or spreadsheets. 3️⃣ Geopolitical Resilience: Europe gains autonomy from unilateral extraterritorial actions. Like the one that silenced the ICC. The #Message Is Clear: Without control over digital identity, there is no digital sovereignty. Building Europe’s own digital identity and trust infrastructure is a super urgent strategic necessity, for economic resilience, democratic integrity, and cybersecurity in the age of digital conflict. Go deeper: https://lnkd.in/e8CPdEEz #DigitalSovereignty #EUBW #EUDIWallet #SSI #TrustInfrastructure #CyberSecurity #EuropeFirst #VerifiableCredentials

Trust is the backbone of every system—human or technical. But unlike encryption, there’s no patch for broken trust. In leadership and cybersecurity, trust is your firewall. It’s what keeps the good actors in and the bad actors out. It’s what empowers a team to report a mistake before it becomes a breach. It’s what keeps your people from walking out the door with your IP in their back pocket and a new job offer in the other. But here’s a little something no one likes to say out loud (or actually do): If you’re going to make claims—have the damn receipts. Don’t promise transparency and then ghost your own employees. Don’t preach integrity while covering up the real cause of a security incident. Don’t say “people are our greatest asset” if you treat them like disposable endpoints. Trust is built through policies and people. Through security awareness and psychological safety. Through owning mistakes and not just spinning them into a comms plan. The most dangerous insider isn’t always malicious. Sometimes it’s the loyal employee you burned. Sometimes it’s the one who trusted you… until you made that impossible. So if you want to lead well— If you want to secure not just your systems but your culture— Start here: ✔️Keep your promises. ✔️Document your claims. ✔️Listen like trust depends on it—because it does. ✔️And when trust is broken, don’t pretend it isn’t. Rebuild it. Brick by brick. Because in cybersecurity and leadership, even in personal relationships, once trust is gone the breach is already in progress. Once trust is broken, transparency is no longer optional. You don’t get to rebuild it on vibes. You rebuild it on accountability. You rebuild it by being ruthlessly consistent. You rebuild it by showing—not telling. (Actions speak louder than words!) So if you’re tempted to shade the truth, inflate your credibility, or coast on charm alone, just remember: Trust isn’t given. It’s loaned. With interest. And people are keeping score. #trust #brokentrust #cybersecurity #leadership #spycraftfortheheart

Congratulations! You're now responsible for raising an AI toddler. Bad news: It's learning all your worst habits. Think of AI like a child that never stops learning, except its entire universe is the data we feed it.  Every bias, every prejudice, every flawed decision we've made becomes part of its DNA. These aren't just technical glitches. They're the unconscious lessons we're teaching our AI children. Here are the 7 deadly biases you need to spot: ⚖️ 1. Training Data Bias - When AI inherits society's past prejudices - Example: Hiring algorithms favouring male candidates because they were trained on historically male-dominated data - Silent but powerful: The bias looks "objective" whilst amplifying discrimination 🔍 2. Representation Bias - When certain groups are missing or misrepresented in training data - Example: Facial recognition failing on darker skin tones - Risk: Systems claim universal accuracy whilst systematically failing specific communities ⚙️ 3. Measurement Bias - When success metrics miss the bigger picture - Example: Optimising for clicks leads to sensationalised content - Impact: Algorithms chase the wrong goals, distorting user experience 🧠 4. Aggregation Bias - The dangerous "one-size-fits-all" assumption - Example: Healthcare algorithms that underperform for minority groups - Reality: What works for the majority might fail for specific populations 🧩 5. Deployment Bias - Using AI in contexts it wasn't designed for - Example: Urban crime prediction tools deployed in rural areas - Danger: Silent failures that reinforce flawed decision-making 🕳️ 6. Automation Bias - Our tendency to trust machines over human judgement - Example: Doctors deferring to AI despite their expertise - Challenge: Human oversight becomes passive as AI decisions go unquestioned 🤖 7. Confirmation Bias - When we only trust AI that confirms our beliefs - Example: Recruiters using AI to justify biased hiring practices - Warning: AI becomes an echo chamber rather than an objective tool Planning to buy AI tools? Here's your bias-prevention checklist:  🗨️ Ask Tough Questions - What data was used to train this AI? - Which demographics were included in testing? - Can you show me performance metrics across different user groups? ⚖️ Demand Transparency - Request bias audit reports - Ask for regular performance monitoring - Ensure you can override AI decisions 🎯 Start Small - Test in limited scenarios first - Compare results with human decisions - Document any concerning patterns Remember: You wouldn't hire a biased employee. Apply the same standards to your AI tools. What questions do you ask before adopting AI tools? Share your checklist 👇 --- 🔔 Follow Alex Issakova for more insights on responsible AI adoption ♻️ Share to raise awareness about AI bias