Recruitment & HR

LinkedIn just responded to the bias claims. They think they refuted my research. I believe they just confirmed it. Following the recent discussions on whether the algorithm suppresses women's voices, LinkedIn's Head of Responsible AI and AI Governance, Sakshi Jain, posted a new Engineering Blog post to "clarify" how the feed works (link in comments). I’ve analysed the post. Far from debunking the issue, it inadvertently confirms the exact mechanism of Proxy Bias I identified in my report (link in comments). Here is the breakdown: 1. The blog spends most of its time denying that the algorithm uses "gender" as a variable. And I agree. My report never claimed the code contained if gender == female. That would be Direct Discrimination. I have always argued this is about Indirect Discrimination via proxies. 2. Crucially, the blog explicitly lists the signals they do optimise for: "position," "industry," and "activity." These are the exact proxies my report flagged. -> Industry/Position: Men are historically overrepresented in high-visibility industries (Tech/Finance) and senior roles. Optimising for these signals without a fairness constraint systematically amplifies men. -> Activity: The (now-viral) trend of women rewriting profiles in "male-coded" language (and seeing 3-figure percentage lift) proves that the algorithm’s "activity" signal favours male linguistic patterns ("agentic" vs. "communal"). 3. The blog confirms the algorithm is neutral in intent (it doesn't see gender) but discriminatory in outcome (because it optimises for biased proxies). In the UK, this is the textbook definition of Indirect Discrimination under the Equality Act 2010. In the EU, this is a Systemic Risk under the Digital Services Act (DSA). LinkedIn has proven that they can fix this. Their Recruiter product uses "fairness-aware ranking" to mitigate these exact proxies (likely for AI Act compliance). The question remains: Why is that same fairness framework not being applied to the public feed? 👉 What We Are Doing About It Analysis is important, but action is essential. I am proud to support the new petition, "Calling for Fair Visibility for All on LinkedIn". This isn't just a complaint; it’s a demand for transparency. We are calling for an independent equity audit of the algorithm and a clear mechanism to report unexplained visibility collapse. If you are tired of guessing which "proxy" you tripped over today, join us and sign the petition (link in the comments).

I Can Spot a Great Candidate in 30 Seconds - Without Looking at Their Resume. At Vicco Laboratories, the first few interview rounds are handled by our HR and leadership team. They assess skills, experience, performance history - all the standard checkboxes. But when someone reaches my room, I’m not evaluating capability. I’m evaluating character. Because skills can be trained. Character can’t. So in the final round, I deliberately observe three things before we even get into formal questions: 𝐓𝐫𝐚𝐢𝐭 1: 𝐇𝐨𝐰 𝐓𝐡𝐞𝐲 𝐓𝐫𝐞𝐚𝐭 𝐭𝐡𝐞 𝐒𝐦𝐚𝐥𝐥𝐞𝐬𝐭 𝐏𝐞𝐫𝐬𝐨𝐧 𝐢𝐧 𝐭𝐡𝐞 𝐑𝐨𝐨𝐦 Before they enter, I always ask our receptionist to make them wait for a few minutes. Not to trouble them — but to observe: Do they greet her or ignore her? Do they show gratitude or entitlement? Do they smile or stay blank? Do they thank her when being called in? If someone is only respectful upwards, they’re not fit for leadership. 𝐓𝐫𝐚𝐢𝐭 2: 𝐇𝐨𝐰 𝐓𝐡𝐞𝐲 𝐇𝐚𝐧𝐝𝐥𝐞 𝐒𝐢𝐥𝐞𝐧𝐜𝐞 During the conversation, I pause intentionally. A great candidate: Doesn’t panic when things go quiet Holds eye contact without overcompensating Thinks before responding, instead of rushing to impress Silence is a pressure test.  Silence exposes a person’s comfort with themselves. And self-assured people make better decisions under pressure. 𝐓𝐫𝐚𝐢𝐭 3: 𝐖𝐡𝐞𝐭𝐡𝐞𝐫 𝐓𝐡𝐞𝐲 𝐀𝐬𝐤 “𝐖𝐡𝐚𝐭 𝐂𝐚𝐧 𝐈 𝐆𝐢𝐯𝐞 𝐭𝐨 𝐕𝐢𝐜𝐜𝐨”, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 “𝐖𝐡𝐚𝐭 𝐖𝐢𝐥𝐥 𝐈 𝐆𝐞𝐭?” I watch closely when compensation and responsibilities are discussed. If the questions are only about salary, perks and timings, they’re employees. If they ask about learning culture, values, decision-making structure…they are already thinking as an owner. I’ll always choose alignment over achievement. So if you’re ever preparing for your final round anywhere — don’t just prepare your resume. Prepare your presence. Because long after your words fade, your character stays in the room. Sanjeev Pendharkar  Just sharing what I’ve learnt #values #business #hiring #hr #decisionmaking #cv #leadership #skills

The European Parliament has officially passed Extended Producer Responsibility (EPR) legislation that fundamentally shifts the responsibility for textile waste management to fashion brands and retailers – with far-reaching global implications. This new law requires all producers, including e-commerce platforms, to cover the full cost of collecting, sorting, and recycling textiles, regardless of whether they are based within or outside the EU. The financial burden of Europe's textile waste now falls squarely on the brands that create it. What are the critical business implications? UNIVERSAL SCOPE: The legislation applies to all producers selling in the EU market, including those of clothing, accessories, footwear, home textiles, and curtains. No company is exempt based on location. FAST FASHION PENALTY: Member states must specifically address ultra-fast and fast fashion practices when determining EPR financial contributions, creating cost penalties for unsustainable business models. GLOBAL SUPPLY CHAIN DISRUPTION: As the world's largest textile importer, the EU's new rules will ripple across global supply chains, particularly impacting exporters from Bangladesh, Vietnam, China, and India who supply much of Europe's fast fashion. TIMELINE PRESSURE: Officially adopted September 2025, this creates immediate operational and financial planning requirements. COMPETITIVE RESHAPING: Brands and retailers will inevitably pass increased costs down their supply chains, fundamentally altering supplier relationships and pricing structures globally. What are the implications for various stakeholders? For CEOs and board members: This represents more than regulatory compliance – it's a complete business model transformation. Companies must now integrate end-of-life costs into product pricing, rethink supplier partnerships, and accelerate circular design strategies. For sustainability and decarbonisation executives: This creates unprecedented opportunities for circular economy solutions, sustainable material innovation, and traceability system development across global supply chains. Link: https://lnkd.in/dTyHtHuD #sustainablefashion #circulareconomy #textilwaste #epr #fashionindustry #sustainability #supplychainmanagement #fastfashion #environmentalregulation #businessstrategy #decarbonisation #textilerecycling #fashionceos #boardgovernance #climateaction #wastemanagement #producerresponsibility #fashionsustainability #textileindustry #greenbusiness

"The language of #diversity, #equity, and #inclusion might change, but impactful work will not." This was the hopeful refrain of many as anti-DEI backlash and political attacks ramped up against this critical work. But as the months drew on, I wasn't seeing any compelling new language. Leaders were watching and waiting, hoping that a new framework would organically emerge that could protect our impact while being more defensible against political attacks. So I started creating that framework myself. The FAIR Framework, standing for Fairness, Access, Inclusion, and Representation, officially launches today in a new feature article for the Harvard Business Review. I wanted to create something that could build on the best of effective DEI work, discard the performative noise, and be firmly comprehensible and defensible by any leader. And after countless hours of research, it boiled down to 4 tenets: 🎯 Outcomes-Based, focused on measurable results rather than flimsy signals of commitment. 🌐 Systems-Focused, using change management to shift workplace systems, rather than surface-level awareness. 🔗 Coalition-Driven, seeking to engage the collective rather than delegating the burden of blame or change onto cliques. 🌱 Win-Win, communicating the benefits of healthier organizations for everyone, rejecting zero-sum framing. FAIR work looks like challenging discrimination in pay, hiring, and promotions, and ensuring that workplace systems set everyone up to succeed. FAIR work looks like removing barriers to participation, using universal design principles to build for all, and including users in every design process. FAIR work looks like creating a workplace culture that recognizes people's differences and ensures a high standard of respect, value, and safety for all. FAIR work looks like participatory decision-making, transparent communications, and strong track records of promises kept and trust maintained. I designed FAIR to be something any leader and practitioner can use—so long as your work meets the core tenets. If I'm being frank, however, a good deal of work calling itself "DEI" does not pass the test. The feel-good trainings with no impact measurement, the never-ending coaching services trying to "fix" the individual but never the systems holding them back, the blame-and-shame strategies that trade a moment of vindication for months of backlash; if we are to survive this moment, we cannot take this kind of "DEI" work with us. I put this framework out into the world with a healthy dose of pride and anxiety. It is far from perfect. It will certainly evolve as practitioners iterate and improve on it. But I truly believe that this is exactly the kind of rigorous, defensible framework leaders need right now to weather this storm and emerge with their impact intact. I hope you find it useful as you seek to do the same. A free gift link is in the comments—please share if it resonates.

Louder for the people at the back 🎤 Many organisations today seem to have shifted from being institutions that develop great talent to those that primarily seek ready-made talent. This trend overlooks the immense value of individuals who, despite lacking experience, possess a great attitude, commitment, and a team-oriented mindset. These qualities often outweigh the drawbacks of hiring experienced individuals with a fixed and toxic mindset. The best organisations attract talent with their best years ahead of them, focusing on potential rather than past achievements. Let’s be clear this is more about mindset and willingness to learn and unlearn as apposed to age. To realise the incredible potential return, organisations must commit to creating an environment where continuous development is possible. This requires a multi-faceted approach: 1. Robust Training Programmes: Employers should invest in comprehensive training programmes that equip employees with the necessary skills for their roles. This includes on-the-job training, mentorship programmes, online courses, and workshops. 2. Redefining Hiring Criteria: Organisations should revise their hiring criteria to focus more on candidates’ potential and willingness to learn rather than solely on prior experience or formal qualifications. Behavioural interviews, aptitude tests, and probationary periods can help assess a candidate's ability to learn and adapt. 3. Partnerships with Educational Institutions: Companies can collaborate with educational institutions to design curricula that align with industry needs. Apprenticeship programmes, internships, and cooperative education can bridge the gap between academic learning and practical job skills. 4. Lifelong Learning Culture: Encouraging a culture of lifelong learning within organisations is crucial. Employers should provide ongoing education opportunities and support for professional development. This includes continuous skills assessment and access to resources for upskilling and reskilling. 5. Inclusive Recruitment Practices: Employers should implement inclusive recruitment practices that remove biases and barriers. Blind recruitment, diversity quotas, and targeted outreach programmes can help ensure that diverse candidates are given a fair chance. By implementing these measures, organisations can develop a workforce that is adaptable, innovative, and resilient, ensuring sustainable success and growth.

Most people get Reference Checks wrong! Here's how to get them right 👉🏻 Throughout my journey, I've had to make 1000s of hires and often struggled with evaluation through the standard interviewing processes. I read somewhere that ~60% senior hires go wrong even after the most meticulous processes so I wondered how to improve the odds. 🤔 What I discovered is that there's no substitute for spending time with the candidates and conducting ‘unnamed’ ref checks through your own network. But what I also learnt is that not every ref check is the same and you can end up with very different outcomes depending on how it’s done. So, through reading and experience, I came with the best practices that I christened with the acronym "PEARL", and here it is for the FIRST time🔥 P - Promise Reciprocity Busy professionals don't dole out intel freely. So, you must offer to return the favor – something as simple as “If ever you need my help for a ref check or otherwise, I'd be happy to help". A senior leader will immediately see its value & perhaps become more ‘available’ on the call. E - Ensure Confidentiality This is critical, especially in India. Candor is not part of our culture, so assure the referrer that you understand the sensitivity of this call and will keep it 100% confidential. Also that you'd expect the same if they ever choose to call you for a reference. If you still sense some hesitancy, maybe throw an ‘offer’ of a good-faith NDA. Don’t worry, nobody ever takes it up but it makes them less guarded. A - Ask questions that force specificity (close-ended & open-ended) Broad questions like – "How was their work ethic?" “Does she work hard?” - are a complete waste of time. You need to ask 2nd order questions that make it comfortable for the referrer to answer without feeling like they're maligning the candidate. For eg - “How do you think we can help the candidate grow?" is better than "Can you tell me about their weaknesses?” R - Retrieve critical insights Actively listen and probe for specifics. Did the candidate consistently meet deadlines? Why or why not? How did they handle pressure? Did they run towards solving problems or look for directions to carry out? These details paint a picture beyond the resume. L - Learn rehire potential And finally, the golden question – "Are you willing to re-hire or work with the candidate again? Why or why not?" Regardless of what the referrer may have said up to this point, most senior folks will have a hard-time giving you a false or misleading response to this one. This is the true gauge of the candidate’s potential and one I put a lot of weight in. To conclude, thank the referrer for their time, assure confidentiality again and commit to a quid pro quo. This leaves the door open for other ref checks you might wish to do in the future 😏 So, there you have it - A PEARL from my collection🙌🏻 Do comment with something that’s worked for you that I may have missed :) #hiring #startups #leadership

If you’re an AI engineer trying to understand and build with GenAI, RAG (Retrieval-Augmented Generation) is one of the most essential components to master. It’s the backbone of any LLM system that needs fresh, accurate, and context-aware outputs. Let’s break down how RAG works, step by step, from an engineering lens, not a hype one: 🧠 How RAG Works (Under the Hood) 1. Embed your knowledge base → Start with unstructured sources - docs, PDFs, internal wikis, etc. → Convert them into semantic vector representations using embedding models (e.g., OpenAI, Cohere, or HuggingFace models) → Output: N-dimensional vectors that preserve meaning across contexts 2. Store in a vector database → Use a vector store like Pinecone, Weaviate, or FAISS → Index embeddings to enable fast similarity search (cosine, dot-product, etc.) 3. Query comes in - embed that too → The user prompt is embedded using the same embedding model → Perform a top-k nearest neighbor search to fetch the most relevant document chunks 4. Context injection → Combine retrieved chunks with the user query → Format this into a structured prompt for the generation model (e.g., Mistral, Claude, Llama) 5. Generate the final output → LLM uses both the query and retrieved context to generate a grounded, context-rich response → Minimizes hallucinations and improves factuality at inference time 📚 What changes with RAG? Without RAG: 🧠 “I don’t have data on that.” With RAG: 🤖 “Based on [retrieved source], here’s what’s currently known…” Same model, drastically improved quality. 🔍 Why this matters You need RAG when: → Your data changes daily (support tickets, news, policies) → You can’t afford hallucinations (legal, finance, compliance) → You want your LLMs to access your private knowledge base without retraining It’s the most flexible, production-grade approach to bridge static models with dynamic information. 🛠️ Arvind and I are kicking off a hands-on workshop on RAG This first session is designed for beginner to intermediate practitioners who want to move beyond theory and actually build. Here’s what you’ll learn: → How RAG enhances LLMs with real-time, contextual data → Core concepts: vector DBs, indexing, reranking, fusion → Build a working RAG pipeline using LangChain + Pinecone → Explore no-code/low-code setups and real-world use cases If you're serious about building with LLMs, this is where you start. 📅 Save your seat and join us live: https://lnkd.in/gS_B7_7d

You're in a job interview, you get the offer—but the salary? Way lower than expected. The worst move? Accepting on the spot. The second worst? Declining outright. Here's how you can take the 'ick' out of negotiating: 1. Start with Gratitude →“Thank you for the offer.” 2. Share Excitement →“I’m really excited about the role and joining the company.” 3. Address the Salary →“Before I accept, I’d like to discuss the salary. It’s below what I believe reflects the market value for my experience.” 4. Reinforce Your Value →“I’m confident my expertise in A and B, and my contributions to C and D will drive success here.” 5. Reiterate Market Value →“Based on my research and track record, I believe a salary range of X to Y would be more in line with the industry.” Where to do research? Check salary data on sites like Glassdoor, Payscale, and LinkedIn, or ask industry peers and recruiters for real-world insights. Pro tip: Use multiple sources to get a well-rounded view and always adjust for location and years of experience. P.S. Have you ever accepted a salary because you didn't know how to negotiation? I'll go first: Yes, I have...

This isn't just another corporate restructuring. It's different this time: → These aren't juniors - they're cutting SENIOR roles → Many have 5+ years of experience → This is happening during peak consulting season Why?: → AI does in minutes what took analysts weeks → Clients now have their own data teams → SaaS platforms replaced implementation work → Premium fees are compressing as analysis gets commoditized The future of consulting: → Small, elite teams replace massive pyramids → On-demand talent replaces fixed benches → Only truly strategic work survives For the Big 4 firms holding onto the old model? EY just showed us their future. The question isn't whether consulting will change. It's whether they can change fast enough.

This is the single most important paper to come out in our sector in recent weeks. Erik Brynjolfsson, Bharat Chandar and Ruyu Chen investigate whether generative AI is leading to job losses in roles most exposed to AI – and how these effects differ by age and the way AI is used. Key findings: → Young workers (ages 22-25) in high-AI-exposure jobs like software development and customer service experienced a 6% absolute drop in employment since late 2022, while employment for workers aged 35-49 grew by over 9% → This pattern only appears in jobs where AI automates work - jobs where AI augments human capabilities showed no employment decline for young workers → The changes are visible in hiring patterns rather than wages, suggesting companies are hiring fewer entry-level workers rather than cutting pay AI may be creating a two-tier job market. I will be looking deeper into this in Exponential View in the coming days and weeks.