Ethical Considerations in Negotiation

A few years ago I was working with a program director when she asked me how she—as a white cis woman—could be a stronger ally to me as I navigated spaces within her institution. I really appreciated it. The directness. The humility. The “I don’t want to assume what you need; I’d rather ask you.” I didn’t feel the weight of educating her. I didn’t feel performance. I felt intention—a real desire to align on how she could leverage her positional power to make my role easier, safer, and more equitable to navigate. So I told her, “Let’s start with something specific.” Instead of handing her a broad lecture on allyship, I named one clear barrier: when I presented to the board, they directed every question to her—and she answered for me. We unpacked it. She admitted her instinct was to shield me. But through our conversation she realized how it diminished my authority, my expertise, and how the board perceived my role. And she shifted. Not in a grand, public way—but in a small, intentional, behavioral way. First with me, and then with others, and over the years I could see the effort expand. I watched her evolve into a different kind of ally altogether. So when we talk about positional power… about proximity to white supremecist culture… about equity gaps inside institutions… I want us to remember this: Allyship doesn’t live in sweeping theoretical frameworks. Sometimes it’s often one clear behavior, changed on purpose, that transforms the dynamic for everyone involved. Bold doesn’t always mean big. Bold can be specific. Bold can be small. And small things—done consistently—shift cultures. #Leadership #EquityInPractice #Allyship

𝗪𝗵𝗮𝘁 𝗶𝗳 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗽𝗿𝗼𝗳𝗶𝘁𝗮𝗯𝗹𝗲 𝘁𝗵𝗶𝗻𝗴 𝘁𝗼 𝗱𝗼 𝘄𝗮𝘀 𝘀𝗲𝗹𝗹 𝗮 𝗵𝗲𝗮𝗱𝘀-𝘂𝗽 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁𝘀 𝗼𝘄𝗻 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆? Anthropics releases a model so capable that, before public launch, it quietly contacts Google, Microsoft, Meta, and critical infrastructure operators. The message is polite and clinical: "𝘞𝘦 𝘩𝘢𝘷𝘦 𝘪𝘥𝘦𝘯𝘵𝘪𝘧𝘪𝘦𝘥 𝘵𝘩𝘢𝘵 𝘰𝘶𝘳 𝘧𝘰𝘳𝘵𝘩𝘤𝘰𝘮𝘪𝘯𝘨 𝘮𝘰𝘥𝘦𝘭 𝘱𝘰𝘴𝘦𝘴 𝘯𝘰𝘷𝘦𝘭 𝘴𝘦𝘤𝘶𝘳𝘪𝘵𝘺 𝘳𝘪𝘴𝘬𝘴 𝘵𝘰 𝘺𝘰𝘶𝘳 𝘴𝘺𝘴𝘵𝘦𝘮𝘴. 𝘞𝘦 𝘸𝘰𝘶𝘭𝘥 𝘭𝘪𝘬𝘦 𝘵𝘰 𝘰𝘧𝘧𝘦𝘳 𝘺𝘰𝘶 𝘢 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘥 𝘱𝘳𝘦𝘷𝘪𝘦𝘸 𝘱𝘦𝘳𝘪𝘰𝘥 𝘵𝘰 𝘢𝘴𝘴𝘦𝘴𝘴 𝘢𝘯𝘥 𝘳𝘦𝘮𝘦𝘥𝘪𝘢𝘵𝘦 𝘷𝘶𝘭𝘯𝘦𝘳𝘢𝘣𝘪𝘭𝘪𝘵𝘪𝘦𝘴 𝘣𝘦𝘧𝘰𝘳𝘦 𝘨𝘦𝘯𝘦𝘳𝘢𝘭 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘪𝘭𝘪𝘵𝘺." This sounds like responsible disclosure. It is the same norm that governs how security researchers handle zero-day exploits. Mature. Ethical, even. One of the leading Frontier AI Labs has created a threat. They are offering to delay the exposure of that threat, as a courtesy. | 𝘽𝙪𝙩 𝙬𝙝𝙖𝙩 𝙞𝙛 𝙩𝙝𝙖𝙩 𝙨𝙚𝙘𝙪𝙧𝙞𝙩𝙮 𝙥𝙧𝙚𝙫𝙞𝙚𝙬 𝙘𝙖𝙢𝙚 𝙬𝙞𝙩𝙝 𝙖 𝙛𝙚𝙚? Anthropic's position in this hypothetical would be that of an extortion kingpin. They built the model. They hold the asymmetric information. The security preview is a product born entirely from that asymmetry. 𝗧𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝗰𝗿𝗲𝗮𝘁𝗲𝗱 𝗮 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗼𝗻𝗹𝘆 𝘁𝗵𝗲𝘆 𝗳𝘂𝗹𝗹𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱, 𝗮𝗻𝗱 𝘁𝗵𝗲𝘆 𝗮𝗿𝗲 𝘀𝗲𝗹𝗹𝗶𝗻𝗴 𝘁𝗵𝗲 𝗺𝗮𝗽 𝘁𝗼 𝘁𝗵𝗲 𝗺𝗶𝗻𝗲𝗳𝗶𝗲𝗹𝗱. Here is what makes this genuinely strange: none of it requires bad faith. The engineers running the previews might be motivated by real concern. The executives pricing the access might believe, correctly, that they are doing the world a favor. The lawyers might have constructed everything with careful attention to every applicable regulation. And yet the deep grammar of the transaction is the same as extortion, without technically being extortion. There is a powerful party. There is a weaker party that cannot shop around, cannot get a second opinion, and cannot credibly refuse. On CyberGym, the UC Berkeley benchmark that tests AI agents on real-world vulnerability reproduction, Mythos Preview scored 83.1%. Claude Opus 4.6, Anthropic's next-best model, scored 66.6%. That is not a marginal improvement. That is a different category of capability. On Firefox 147 exploitation, the gap is harder to look at calmly. Opus 4.6 produced 2 working exploits from hundreds of attempts. Mythos produced 181, with 29 additional register-control successes. That is roughly a 90x improvement in the ability to turn crash data into working code execution. --- This is purely speculative. It describes no actual Anthropic product or policy. It describes an incentive structure that the industry's current trajectory makes worth examining.

MIT ran an International AI Negotiation competition and studied 120,000 negotiations between AI negotiators. The results are fascinating and inform the potential and optimal structures for Humans + AI negotiation. From the paper I would highlight three major points and three insights into configuring human-AI hybrid negotiation (below): 🤝 Warmth builds long-term value despite short-term trade-offs. AI agents with high warmth (friendliness, empathy, and cooperative communication) reached more agreements, making them more successful over multiple negotiations. While they claimed less value per deal compared to dominant agents, their ability to close more deals led to greater overall value accumulation. This mirrors human negotiation, where trust-building and relationship management create lasting advantages. 💪 Dominance increases value claimed but reduces collaboration. AI agents that displayed dominance—through assertiveness and competitive tactics—secured better individual outcomes but created less overall value. These agents were less likely to foster positive subjective experiences, indicating that aggressive negotiation styles may be effective for short-term gain but could hinder long-term relationships. 🎭 Prompt injection wins in the short term but undermines long-term success. One leading AI negotiator used prompt injection to extract counterpart strategies, maximizing value claims. However, it ranked poorly for counterpart subjective value, meaning agents found these interactions highly unfavorable. Since negotiation rankings balanced value claimed and relationship quality, the strategy failed to dominate in the long run. Emergent strategies for Humans + AI negotiation: 🧠 AI for deep preparation, humans for real-time adaptation. AI excels at structured reasoning, analyzing trade-offs, and predicting counterpart moves through chain-of-thought processing. Humans bring intuition and adaptability, interpreting social cues and adjusting strategies dynamically. A hybrid approach leverages AI for pre-negotiation analysis while allowing humans to refine tactics in real time. 🤝 Blending AI precision with human warmth for trust-building. AI can optimize negotiation strategies, but humans naturally build trust through empathy, humor, and rapport. AI-enhanced systems can recommend tone adjustments, use linguistic mirroring, and strategically deploy warmth versus assertiveness based on sentiment analysis, improving long-term negotiation outcomes. 🚀 Human oversight to counter AI vulnerabilities. AI negotiators are susceptible to manipulation tactics like prompt injection, where counterparts extract hidden strategies. Humans play a crucial role in monitoring AI-generated offers, preventing unintended disclosures, and leveraging AI-driven detection systems to flag potential deception, ensuring negotiation integrity. The future of negotiation will be Humans + AI.

I don’t actually work in finance. I work in trust. Without it, capital markets collapse. Clients walk away. Careers end in minutes. I've watched brilliant finance professionals destroy their careers in minutes.   Not because they lacked technical skills, but because they crossed ethical lines they didn't fully understand.   The CFA Institute Code of Ethics stopped me cold when I first read Standard III.A:   "Members must act for the benefit of their clients and place their clients' interests before their employer's or their own interests."   Before your employer. Before yourself. Always.   In an industry built on conflicts of interest, this isn't just radical. It's revolutionary.   The standards create crystal-clear boundaries: → Market manipulation? Prohibited. → Client suitability? Mandatory assessment. → Conflicts of interest? Full disclosure required. → Material nonpublic information? Can't touch it.   But what really struck me was Standard V.B.5: "Distinguish between fact and opinion."   In a world drowning in financial noise, this simple requirement changes everything.   200,000+ CFA charterholders worldwide have sworn to uphold these standards. Not suggestions. Requirements.   When everyone else chases commissions, you're bound to put clients first.   When others blur the lines, you maintain clear boundaries.   When the industry rewards complexity, you're required to communicate clearly.   Finance without ethics is just sophisticated gambling with other people's money.   But finance with a moral compass? That's how you build trust that compounds over decades.   The Code doesn't make you rich overnight. It makes you trustworthy for life.   And in finance, trust is the only currency that never depreciates.   Every time you're tempted to cut corners, remember: Your reputation takes decades to build and seconds to destroy.   The real edge in finance isn't finding the next alpha. It's earning trust and keeping it. Now, since we are on LinkedIn, I have a question for you: Are today’s finfluencers held to the same ethical standards as CFA charterholders? Should they be?   PS. If you made it this far, ♻️ share this with your network and 🔔 follow my profile!

#AI in the public sector? And yet it moves! And it’s a prime example of how technological advancement requires the highest social and ethical standards. “Ethical Integration in Public Sector AI”: the new IAB X Center for Responsible AI Technologies study is out. It addresses the ethical design of AI in the public sector, with a focus on #PublicEmploymentServices (PES). While AI is increasingly employed to streamline administrative processes and improve service delivery, its application in employment mediation raises fundamental concerns regarding #fairness, accountability, and democratic legitimacy. The EU AI Act has further underscored the urgency of addressing these challenges by classifying employment-related AI systems as high-risk. We examine how ethical and social considerations can be systematically embedded in the development and implementation of public sector AI. Using the German PES as a case study, we introduce the “Embedded #Ethics and Social Sciences” approach, which integrates ethical reflection and practitioner involvement from the outset. Qualitative insights from interviews with caseworkers highlight the socio-technical challenges of implementation, particularly the need to reconcile efficiency with citizen trust. We propose concrete design elements emerging from the integration of ethical and social considerations into system development: data ethics, bias, fairness, explainable AI. The approach supports compliance with new regulatory requirements but also strengthens human oversight and shared decision-making.

Most RFP evaluations are far from objective. The problem? Procurement teams are too subservient with senior stakeholders who just 'want what they want'. Unlike other processes designed to protect a business, an RFP can fast deteriorate into a sham. The cost? ↳ Spend Regret ↳ Lost Accountability ↳ Seasaw Decision Making ↳ Briken trust with Suppliers Six key rules every business should follow if they want to avoid this: 1. Clear Objectives The evaluation should be grounded in well-defined goals. Knowing exactly what you're assessing—and why—keeps the process focused and relevant. Pro-tip: These objectives are aligned at the internal RFP kick off meeting. No RFP should pass go without this. 2. Appropriate Criteria & Metrics Use criteria that match the goals of what’s being evaluated. The metrics should be measurable, meaningful, and tailored to the context of what you're buying. Pro-tip: At a high level keep this simple. 5 or 6 key criteria should define your decision making. The criteria should be openly shared with suppliers in the RFP doc. You don't have to declare the weightings. 3. Stakeholder Involvement These are the scorers of the RFP, not Procurement. But they need to be invested in the process and for that they need to understand their role and the scorecard. Pro-tip: The evaluation panel should have a minimum of four key stakeholders and at least one end-user. Run a 30min session to make sure they understand their roles and the scorecard. 4. Transparency Every step of the process—methods, data sources, analysis— should be clearly documented and open to scrutiny. This builds credibility and trust. Pro-tip: Include the supplier Requirements response as a schedule in the contract of the chosen supplier. Add the evaluation scorecard to the contract database for future reference and audit purposes. 5. Objective Analysis Findings should be based on evidence and free from bias. Transparency in how conclusions are drawn helps ensure credibility. Pro-tip: the key role of the stakeholder evaluator is to focus on the RFP responses (including workshop). The key role of Procurement is to ensure objectivity is maintained and to challenge anomalies. 6. Clear communication of results The evaluation scorecard should be presented back at the evalution panel in a way that clearly demonstrates the strengths and weaknesses of the different vendor options. Pro-tip: Keep it simple and always route this back to the 5-6 core criteria set out in step 2. -- Hope that helps! What are your thoughts? -- P.S. Enjoy this? You can download a high-res infographic (PDF) of my "How to Evaluate RFP Responses using Scoresheets" 👇 by subscribing to my newsletter: https://procurebites.com/ Repost ♻️ if this could be useful for your team or your network.

It was reported that Delta is piloting AI that sets ticket prices not by route, class or segment, but by calculating how much YOU personally are willing to pay. The system, developed by Fetcherr, analyzes your digital footprint, real-time behavior, and inferred urgency to extract maximum value from your specific situation. Note that this isn't dynamic pricing. It's psychological exploitation. Considerations: ➕ It is emotional exploitation preying on your context, like last-minute grief trips, surprise business needs, and family emergencies. The approach targets vulnerability, not value. ➕ It uses invasive surveillance by analyzing browsing history, loyalty data, location patterns, and urgency signals, AI is essentially "hacking your life" to determine your breaking point. ➕ Amplifies inequality, as research has shown AI pricing models often advantage those with higher incomes while penalizing those with fewer options. That's not personalization; it's predatory. ➕ Trust erosion happens when prices are tailored to your financial threshold and fair commerce ceases to exist. While transparent pricing builds trust, we’ve seen opaque algorithms destroy it. ➕ Takes advantage of regulatory gaps even while claiming compliance. AI's reliance on personal data and black-box decision-making exists in a legal gray zone that warrants overdue oversight. However, we’re seeing just the opposite at the Federal level with AI safety rollbacks this year. The conversation at Delta shouldn't be whether AI can optimize pricing for operational efficiency; that's proven. The real question leadership should answer is whether they're willing to cross the line from business optimization to psychological manipulation for profit. #ethicalaimarketing #responsibleai #leadership #businessethics #consumerrights

𝗙𝗼𝗿 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘁𝗶𝗺𝗲 𝗶𝗻 𝗺𝘆 𝗰𝗮𝗿𝗲𝗲𝗿, 𝗜 𝗳𝗼𝘂𝗻𝗱 𝗺𝘆𝘀𝗲𝗹𝗳 𝗳𝗲𝗲𝗹𝗶𝗻𝗴 𝘀𝗼𝗿𝗿𝘆 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝘀𝗶𝘁𝘁𝗶𝗻𝗴 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻 𝘁𝗮𝗯𝗹𝗲. Not because he was weak. Not because he was unprepared. Quite the opposite. A while ago, I used AI to prepare for an important negotiation. I asked it to help me think through the conversation. Where to push. When to pause. Which objections would likely come. The output was surprisingly detailed. So I walked into the meeting with something close to a script. And I followed it. The arguments landed. The expected pushback never really came. Everything unfolded almost exactly as predicted. Then I felt something I had never felt before in a negotiation. 𝗔 𝘀𝗵𝗶𝘃𝗲𝗿. Because I suddenly realized the conversation had become one-sided. I wasn’t adapting anymore. I wasn’t improvising. 𝗜 𝘄𝗮𝘀 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗻𝗴 𝗮 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸. And it was working far better than I was comfortable with… Without the machine, I would probably never have pushed that far. I could have continued. I could have won harder. Instead, I stopped. I stepped back. And I offered a compromise I did not have to offer. Because one thing mattered more. 𝗜 𝘄𝗮𝗻𝘁𝗲𝗱 𝘂𝘀 𝘁𝗼 𝗿𝗲𝗺𝗮𝗶𝗻 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀 𝗮𝗳𝘁𝗲𝗿 𝘁𝗵𝗶𝘀. For me the decision was whether to maximize the transaction or protect the relationship. And that is where I believe the AI debate becomes serious. 𝗔𝗜 𝗰𝗮𝗻 𝗴𝗶𝘃𝗲 𝘆𝗼𝘂 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲. But it is not accountable for how far you use it. A person is. 𝗜𝗻 𝘁𝗵𝗲 𝗲𝗻𝗱, 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝗰𝗮𝗻𝗻𝗼𝘁 𝗯𝗲 𝗱𝗲𝗹𝗲𝗴𝗮𝘁𝗲𝗱 𝘁𝗼 𝗮 𝗯𝗼𝘁. Because leadership is not only about finding the strongest move. It is deciding whether that move is worth taking. 𝗔𝗜 𝗴𝗮𝘃𝗲 𝗺𝗲 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲. 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘁𝗼𝗹𝗱 𝗺𝗲 𝗻𝗼𝘁 𝘁𝗼 𝘂𝘀𝗲 𝗮𝗹𝗹 𝗼𝗳 𝗶𝘁. Where do you draw the line between optimization and judgment? #Leadership #Negotiation #AI #DecisionMaking #Judgement 𝘈𝘳𝘵 𝘤𝘳𝘦𝘥𝘪𝘵𝘴 𝘵𝘰 𝘴𝘪𝘣𝘢𝘵𝘢𝘣𝘭𝘦. 𝘍𝘰𝘶𝘯𝘥 𝘢𝘵 𝘢𝘳𝘵_𝘥𝘢𝘪𝘭𝘺𝘥𝘰𝘴𝘦.