Writing Informative White Papers

Writing "'Simplifying' European AI Regulation" felt a bit like trying to square the circle. It is an attempt at disentangling some of the threads making up EU digital regulations. We were glad to present our findings at the AI Office two weeks ago. Together with Robert Kilian and my colleague Jana Costas, I set out to ask: how can we make the AI Act simpler - not weaker - while keeping its protective core intact? The answer lies in clarification and disentanglement, I believe. Our new white paper rests on extensive empirical research: 15 in-depth interviews and a focus group with leading European companies and civil society organizations. Their message is remarkably consistent. The problem is not the AI Act itself, but its entanglement with other EU laws, from the Medical Device Regulation and the Machinery Regulation to financial services and the GDPR. We translate these insights into ten evidence-based reform proposals: – Clarify prohibitions by explicitly banning ex-post biometric identification in public spaces, with exceptions for serious crime, like in real-time RBI. – Extend value chain cooperation duties to include GPAI model providers so that downstream developers - often SMEs - can compel cooperation and access necessary compliance information from upstream providers. This is truly key for AI use in high-risk settings. – Adopt a more sectoral approach by . shifting credit, insurance, and employment use cases from Annex III into sector-specific regimes that already regulate these areas in depth . moving sectors like medical and machinery from Annex I A to B. But only with guarantees that updates are indeed made respecting the principles of the AI Act. – Reform technical requirements by replacing the “accuracy” metric with “performance,” and expanding Article 10(5) to all AI systems and GPAI models so bias testing becomes legally feasible. – Simplify deployer duties under Article 26 to avoid redundant monitoring obligations already covered by tort law. – Ensure fairness and consistency in the Fundamental Rights Impact Assessment by applying it either to all private deployers or none. – Close the GPAI modification gap through a new “Article 25 for GPAI” that defines when fine-tuning and modification create new provider responsibilities, building on the AIO Guidelines. – Clarify open-source rules through a uniform definition, basic transparency obligations, and proportional exemptions for SMEs. – Extend implementation timelines for high-risk systems or install an SME enforcement moratorium. – Support SMEs (and CSOs) with targeted financial aid, e.g. for participation in standardization, and technical assistance. The white paper is available now, and our full report will follow in November 2025. Many thanks to Bertelsmann Stiftung and Asena Soydaş for the generous support! On a personal note: I clearly have even more respect for lawmakers now, who have to juggle a myriad of diverging stakeholder inputs... #aiact #simplification

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

"This white paper offers a comprehensive overview of how to responsibly govern AI systems, with particular emphasis on compliance with the EU Artificial Intelligence Act (AI Act), the world’s first comprehensive legal framework for AI. It also outlines the evolving risk landscape that organizations must navigate as they scale their use of AI. These risks include: ▪ Ethical, social, and environmental risks – such as algorithmic bias, lack of transparency, insufficient human oversight, and the growing environmental footprint of generative AI systems. ▪ Operational risks – including unpredictable model behavior, hallucinations, data quality issues, and ineffective integration into business processes. ▪ Reputational risks – resulting from stakeholder distrust due to errors, discrimination, or mismanaged AI deployment. ▪ Security and privacy risks – encompassing cyber threats, data breaches, and unintended information disclosure. To mitigate these risks and ensure AI is used responsibly, in this white paper we propose a set of governance recommendations, including: ▪ Ensuring transparency through clear communication about AI systems’ purpose, capabilities, and limitations. ▪ Promoting AI literacy via targeted training and well-defined responsibilities across functions. ▪ Strengthening security and resilience by implementing monitoring processes, incident response protocols, and robust technical safeguards. ▪ Maintaining meaningful human oversight, particularly for high-impact decisions. ▪ Appointing an AI Champion to lead responsible deployment, oversee risk assessments, and foster a safe environment for experimentation. Lastly, this white paper acknowledges the key implementation challenges facing organizations: overcoming internal resistance, balancing innovation with regulatory compliance, managing technical complexity (such as explainability and auditability), and navigating a rapidly evolving and often fragmented regulatory landscape" Agata Szeliga, Anna Tujakowska, and Sylwia Macura-Targosz Sołtysiński Kawecki & Szlęzak

The Ultimate Board Meeting Pack Checklist I've sat through countless board meetings in my career working with fast growing companies... and if there's one thing I've learned, your board deck serves a critical purpose - empowering your board to understand your company's financial health, performance, and direction. So what makes a great board pack? Let me break it down for you 👇 ➡️ EXECUTIVE SUMMARY Your exec summary needs to pack a punch with just one page. I always include: -A snapshot of company performance with key wins -Any concerns that need immediate attention -Strategic updates in bullet-point format -High-level financial highlights No fluff, just what matters most. Board members should get the full picture in under 30 seconds. ➡️ FINANCIAL OVERVIEW This is where the numbers tell their story: -P&L Summary showing actuals vs budget/forecast (MTD, QTD, YTD) -Cash position with current balance, burn rate, runway -Balance sheet highlights focusing on key shifts in assets/liabilities When I present these, I always color-code variances so problems jump off the page. ➡️ VARIANCE ANALYSIS Don't just show the numbers, explain them: Focus on top 3-5 significant deviations from budget -Get to the root causes behind variances -Include action items to address issues -Use visuals like bar charts to highlight the biggest gaps My favorite approach? Waterfall charts that show the journey from forecast to actual. ➡️ OPERATIONAL METRICS Numbers beyond the financials matter just as much: -Customer metrics (growth, churn, retention, NRR/GRR) -Sales pipeline and conversion stats -Product/feature engagement for tech companies I like to show 6-month trends for these metrics so the board can spot patterns, not just points. ➡️ STRATEGIC INITIATIVES & ROADMAP The board wants to know where you're going: -Status updates on key projects or product launches -Hiring progress versus the plan -Strategic priorities for next quarter Use simple red/yellow/green indicators to show status at a glance. ➡️ RISKS & CHALLENGES Every company has risk. It's how you communicate & plan for that risks that makes all teh difference in the world -Outline key risks across financial, operational, legal areas -Share your mitigation plans for each -Be transparent - boards value this more than sugar-coating ➡️ ASK FROM THE BOARD Be crystal clear about what you need: -Funding requirements -Strategic advice needs -Hiring referrals -Feedback on potential pivots ➡️ APPENDIX Keep the meeting focused, but have backup: -Detailed financials (P&L, BS, CF) -Org chart with key hires highlighted -Detailed KPIs for those who want to dig deeper === That's my complete board pack checklist - but everyone does it differently. What's your approach to board packs? What sections do you find most valuable? Join the discussion in the comments below 👇

Manufacturing excellence starts when the invisible becomes visible, and decisions are driven by data. I had the privilege of contributing to Onward Partners' latest 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝟒.𝟎 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤 𝐖𝐡𝐢𝐭𝐞𝐩𝐚𝐩𝐞𝐫. Based on 𝟏𝟏𝟑,𝟏𝟗𝟐 𝐝𝐚𝐭𝐚 𝐩𝐨𝐢𝐧𝐭𝐬 across 𝟑𝟖𝟕 𝐚𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭𝐬, let me tell you... the insights are both exciting and a little sobering. A few key insights: • 𝟖𝟏% of companies are stuck at the "Connectivity" stage (Stage 2 of 6), with basic digital infrastructure but no leap toward real-time data integration or process automation. • Only 𝟖% have reached "Visibility" (Stage 3 of 6) — achieving full operational insights driven by real-time analytics and seamless data flow. • Contrary to what you may think — People are ready, but tech is lagging: Companies score 𝟐.𝟕 𝐨𝐮𝐭 𝐨𝐟 𝟔 in culture but only 𝟐.𝟏 𝐨𝐮𝐭 𝐨𝐟 𝟔 in technology. 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐈𝐬 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 Benchmarking matters because you can’t improve what you don’t measure. By comparing your digital transformation maturity against industry leaders, you gain: • 𝐂𝐥𝐚𝐫𝐢𝐭𝐲 on where you stand in the journey toward Industry 4.0. • 𝐅𝐨𝐜𝐮𝐬 on which gaps to address, whether it’s culture, technology, or operations. • 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 to prioritize investments that drive measurable results and stay competitive. To succeed, organizations need to focus on building open, future-ready IIoT architectures, driving data democratization, and fostering collaboration across teams. It's about aligning technology with strategy and breaking barriers between innovation and execution. 𝐂𝐡𝐞𝐜𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝟐𝟎 𝐩𝐚𝐠𝐞 𝐰𝐡𝐢𝐭𝐞𝐩𝐚𝐩𝐞𝐫: https://lnkd.in/djTcbbGV ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Most papers don’t fail on data. They fail on flow. (If readers have to work to reconstruct your logic, they stop trusting your conclusions.) Think of your paper as a guided tour, not a data dump: WHY – Why this problem and gap? HOW – How did you tackle it? WHAT – What did you find? SO WHAT – Why does it matter? If every section and paragraph clearly moves the reader along this path, the paper feels “easy to follow” even when the science is complex. One paragraph = one clear job (problem, gap, method choice, key result, implication). First sentence sets the point; last sentence links to what comes next. Use simple signposts: “Building on this…”, “In contrast…”, “As a result…”, “Taken together…” When in doubt, ask: “Does this sentence bring the reader closer to answering my research question?” If not, cut or move it. If you want feedback, drop in the comments: - Your paper title - Your 3–4 sentence storyline (problem → approach → key result → significance), - The section you feel “doesn’t flow”. I soon be launching research paper surgery in my community where we will go into much more details. Stay tuned! #research #science #publishing #phd #professor #postgraduate #graduate #scientist

No one is waking up at 7am, sipping coffee, thinking, “Wow, I really hope someone explains holistic wealth architecture today.” People want clarity. They want content that feels like a conversation, not a lecture. They want to understand what you’re saying the first time they read it. Write like you're talking to a real person. Not trying to win a Pulitzer. - Use short sentences. - Cut the jargon. - Sound like someone they’d trust with their money, not someone who spends weekends writing whitepapers for fun. Confused clients don’t ask for clarification. They move on. Here’s how to make your content clearer: 1. Ask yourself: Would my mom understand this? If the answer is “probably not,” simplify it until she would. No shade to your mom, she’s just a great clarity filter. 2. Use the “friend test.” Read it out loud. If it sounds weird or overly stiff, imagine explaining it to a friend at lunch. Rewrite it like that. 3. Replace jargon with real words. Say “retirement income you won’t outlive” instead of “longevity risk mitigation strategy.” Your clients are not Googling your vocabulary. 4. Stick to one idea per sentence. If your sentence is doing cartwheels and dragging a comma parade behind it, break it up. 5. Format like you actually want them to read it. Use line breaks. Add white space. Make it skimmable. No one wants to read a block of text the size of a mortgage document. Writing clearly isn’t dumbing it down. It’s respecting your audience enough to make content easy to understand. What’s the worst jargon-filled phrase you’ve seen in the wild? Let’s roast it.

Most papers put reviewers to sleep. Fix your next publication: • Drop 60% of unecessary discussion, focus on key finding • End with "Here's why this matters to you" • Start with conflict, not just background (Get my deep dive here: https://lnkd.in/dssRzYZ8) Data tells. Story sells. Even in academia. Even novel research fails without effective storytelling. Here's what transforms dry data into compelling narratives: 1. Limbic Hook • Share a story about contradicting literature • Open with an unexpected question • Present a counterintuitive finding 2. Narrative Arc Problem → Challenge → Discovery → Contribution Hook them. Build anticipation. Have a big insightful reveal. Resolve it with a satisfying ending. Build tension with problem before revealing results 3. Human Element • Connect data to real-world impact • Show why your audience should care • Include specific examples and outcomes It's not just about presenting the data. It's about telling a story of your discovery. (Image: Katrin Wietek) P.S.: Are you pro or con storytelling in research papers? #research #phd #stories

Savvy executives don’t beat around the bush. They want straight facts, fast. When it comes to their executive resumes, this same strategy must apply. Often, executive resumes are written with a text-heavy approach. Yet the tactic to “include everything ever done” doesn’t work in a modern resume. Nor does burying key facts. Recruiters want a quick read with easy-to-absorb information and proof of ability… and they don’t want to hunt for it. To avoid smothering the audience with unrelated details and burying greatest achievements, employ these 3 simple, modern executive resume approaches: 1. Start Strong with Your Resume Headline and Summary A weak resume opening sets the stage for a weak reaction. Garner attention at the get-go by positioning your unique value-add in a strong headline and compelling summary. Leverage prime resume real estate – the top 1/3 of the file – to showcase why you are the candidate of choice. Be specific with who you are, what you are known for, how you can help the target company, and proof of ability. Keep content succinct and measurable so it can be easily absorbed and understood. Headline example: President and CEO: Manufacturing / Start-Ups and Turnarounds P&L up to $160M | Global Teams of 300+ | 300% Revenue Growth in 3 Years 2. Compel the Reader to Keep Reading with Concise Points Instead of a traditional reverse-chronological resume format, where a reader has to wade through each work experience to identify key facts, consider a combination (also known as hybrid) format and include a dedicated achievements section near the start of the file. An achievements section allows you to highlight your top career achievements and position them near the forefront. Big impacts and hard results are difficult to overlook. ACHIEVEMENTS SNAPSHOT: ·       10.2% Annual Sales Growth Average over 6 Years ·       $160M Global Operations | 465+ Employees ·       46% European Business Expansion in 5 Years ·       $1.8M Single Year Cost-Savings ·       350% Growth to Single Customer Sales in 4 Years 3. Front Load Statements Leading with results and front-loading points throughout the file generates a strong impression, eliminates guesswork, and decreases the risk of important “proof” being overlooked. Shorter statements also pop off the page while still offering loads of value. The difference between the two points below is discernible: Weaker, wordier statement: ·       Developed a product line with new features which helped decrease service by half for all end users while also increasing profits $32M over the course of three years. Succinct, front-loaded statement: ·       Added $32M in new profit over 3 years by developing a differentiated product line, which decreased service time 50% for end users. Front-loaded points in a resume powerfully position strengths while spoon-feeding the reader precisely what they need to know to support their decision. #resume #executiveresume #executivesearch

Hate how boring and time-consuming documentation feels? Yeah, same. But here’s the thing: the more you avoid it, the more you hurt your future self and miss opportunities to showcase your skills properly. So if you want to make documentation less painful (and actually useful), here are 6 tips I use with my clients to make it faster, clearer, and more impactful: 1. Start with an overview What’s the purpose of your project? What problem did it solve? Just 3–4 lines to set the stage. Make it easy for anyone to understand why it matters. 2. Walk through your process Break down the steps: How did you collect the data? How did you clean, analyze, or model it? What tools or methods did you use? This shows how you think and how you solve real-world problems. 3. Add visuals A clean chart > a wall of text. Use graphs, screenshots, and diagrams to bring your work to life. (And bonus: you’ll understand it faster when you come back later.) 4. Show your problem-solving What roadblocks did you hit? How did you fix them? Don’t hide your struggles, highlight them. This is where your value really shines. 5. Summarize your results What did you find? Why does it matter? What’s next? Answer these three questions clearly and your audience will instantly get the impact of your work. 6.  Use a structure that makes sense Try this flow: Introduction → Objectives → Methods → Results → Conclusion → Future Work Simple. Clean. Effective. P.S: After every milestone, take 5 minutes to update your notes, screenshots, or results. Turn it into a habit. ➕ Follow Jaret André for more data job search, and portfolio tips 🔔 Hit the bell icon to get strategies that actually move the needle.