Responsible AI Deployment Best Practices

#GRC Today I led a session focused on rolling out a new Standard Operating Procedure (SOP) for the use of artificial intelligence tools, including generative AI, within our organization. AI tools offer powerful benefits (faster analysis, automation, improved communication) but without guidance, they can introduce major risks: • Data leakage • IP exposure • Regulatory violations • Inconsistent use across teams That’s why a well-crafted SOP isn’t just nice to have .. it’s a requirement for responsible AI governance. I walked the team through the objective: 1. To outline clear expectations and minimum requirements for engaging with AI tools in a way that protects company data, respects ethical standards, and aligns with core values. We highlighted the dual nature of AI (high value, high risk) and positioned the SOP as a safeguard, not a blocker. 2. Next, I made sure everyone understood who this applied to: • All employees • Contractors • Anyone using or integrating AI into business operations We talked through scenarios like writing reports, drafting code, automating tasks, or summarizing client info using AI. 3. We broke down risk into: • Operational Risk: Using AI tools that aren’t vendor-reviewed • Compliance Risk: Feeding regulated or confidential data into public tools • Reputational Risk: Inaccurate or biased outputs tied to brand use • Legal Risk: Violation of third-party data handling agreements 4. We outlined what “responsible use” looks like: • No uploading of confidential data into public-facing AI tools • Clear tagging of AI-generated content in internal deliverables • Vendor-approved tools only • Security reviews for integrations • Mandatory acknowledgment of the SOP 5. I closed the session with action items: • Review and digitally sign the SOP • Identify all current AI use cases on your team • Flag any tools or workflows that may require deeper evaluation Don’t assume everyone understands the risk just because they use the tools. Frame your SOP rollout as an enablement strategy, not a restriction. Show them how strong governance creates freedom to innovate .. safely. Want a copy of the AI Tool Risk Matrix or the Responsible Use Checklist? Drop a comment below.

Future of Privacy Forum enters the Chat(GPT) and publishes helpful checklist for the development of organizational generative AI policies. Key points (broken down into simple action items): 1) Use in Compliance with Existing Laws and Policies for Data Protection and Security TO DO: - Assess whether your internal policies account for planned and permitted use of AI; regularly update - Subject sharing data with vendors to requirements that ensure compliance with relevant US state laws (including the "sale/share" issue). - Ensure (through diligence, contractual provisions, and audit) that vendors support any required access and deletion requests - Designate personnel responsible for staying abreast of regulatory and technical developments. WHY: US regulators said they are already enforcing existing legal violations when AI is used to carry them out 2) Employee Training TO DO: - Remind employees that all existing legal obligations remain; especially in regulated industries - Provider training re: the implications and consequences of using generative AI tools in the workplace and specifically re: responsible use, risk, ethics, bias - Advise employees to avoid inputting sensitive or confidential information into a generative AI prompt unless data is processed locally and/or subject to appropriate controls - Establish a system (pop ups?) to regularly remind individuals of legal restrictions on profiling and automated decision-making, as well as key data protection principles - Provide employee with the contact information for personnel that are responsible for AI and data protection 3) Disclosure TO DO: - Provide employees with clear guidance on (a) when and whether to use organizational accounts for generative AI tools, (b) permitted and prohibited uses of those tools in the workplace - Provide employees with an easy to use system to document their use of these tools for business purposes. Such tools should enable employees to add context around any use, and provide a method to indicate how that use fits into the organizations’ policies - Address whether you require or prohibit the use of organizational email accounts for particular AI services or uses. - Communicate when and how the organization will require employees to disclose whether use of AI tools for internal and/or external work product - Update internal documentation, including employee handbooks and policies, to reflect policies regarding Generative AI use 4) Outputs of Generative AI TO DO: - Implement systems to remind employees of issues with generative AI and remind them to verify outputs of generative AI, including for issues regarding accuracy, timeliness, bias, or possible infringement of intellectual property rights - Check and validate coding outputs by generative AI should for security vulnerabilities. #dataprivacy #dataprotection #AIregulation #AIgovernance #AIPrivacy #privacyFOMO https://lnkd.in/dYwgZ33i

In light of the recent discussions around the European Union's Artificial Intelligence Act (EUAI Act), it's critical for brands, especially those in the fashion industry, to understand the implications of AI usage in marketing and beyond. The EU AI Act categorizes AI risks into four levels: unacceptable, high, limited, and minimal risks. For brands employing AI for marketing content, this predominantly falls under limited risks. While not as critical as high or unacceptable risks, limited risks still necessitate a conscientious approach. Here’s what brands need to consider: Transparency: As the backbone of customer trust, transparency in AI-generated content is non-negotiable. Brands must clearly label AI-generated services or content to maintain an open dialogue with consumers. Understanding AI Tools: It's not enough to use AI tools; brands must deeply understand their mechanisms, limitations, and potential biases to ensure ethical use and compliance with the EUAI Act. Documentation and Frameworks: Implementing thorough documentation of AI workflows and frameworks is essential for demonstrating compliance and guiding internal teams on best practices. Actionable Tips for Compliance: Label AI-Generated Content: Ensure any AI-generated marketing material is clearly marked, helping customers distinguish between human and AI-created content. Educate Your Team: Conduct regular training sessions for your team on the ethical use of AI tools, focusing on understanding AI systems to avoid unintentional risks. Document Everything: Maintain detailed records of AI usage, decision-making processes, and the tools' roles in content creation. This will not only aid in compliance but also in refining your AI strategy. Engage in Dialogue with Consumers: Foster an environment where consumers can express their views on AI-generated content, using feedback to guide future practices. For brands keen on adopting AI responsibly in their marketing, it's important to focus on transparency and consumer trust. Ensure AI-generated content is clearly labeled, allowing consumers to distinguish between human and AI contributions. Invest in understanding AI's capabilities and limitations, ensuring content aligns with brand values and ethics. Regular training for your team on ethical AI use and clear documentation of AI's role in content creation processes are essential. These steps not only comply with regulations like the EU AI Act but also enhance brand integrity and consumer confidence. To learn more about more about EU AI act impact on brands check out https://lnkd.in/gTypRvmu

A New Path for Agile AI Governance To avoid the rigid pitfalls of past IT Enterprise Architecture governance, AI governance must be built for speed and business alignment. These principles create a framework that enables, rather than hinders, transformation: 1. Federated & Flexible Model: Replace central bottlenecks with a federated model. A small central team defines high-level principles, while business units handle implementation. This empowers teams closest to the data, ensuring both agility and accountability. 2. Embedded Governance: Integrate controls directly into the AI development lifecycle. This "governance-by-design" approach uses automated tools and clear guidelines for ethics and bias from the project's start, shifting from a final roadblock to a continuous process. 3. Risk-Based & Adaptive Approach: Tailor governance to the application's risk level. High-risk AI systems receive rigorous review, while low-risk applications are streamlined. This framework must be adaptive, evolving with new AI technologies and regulations. 4. Proactive Security Guardrails: Go beyond traditional security by implementing specific guardrails for unique AI vulnerabilities like model poisoning, data extraction attacks, and adversarial inputs. This involves securing the entire AI/ML pipeline—from data ingestion and training environments to deployment and continuous monitoring for anomalous behavior. 5. Collaborative Culture: Break down silos with cross-functional teams from legal, data science, engineering, and business units. AI ethics boards and continuous education foster shared ownership and responsible practices. 6. Focus on Business Value: Measure success by business outcomes, not just technical compliance. Demonstrating how good governance improves revenue, efficiency, and customer satisfaction is crucial for securing executive support. The Way Forward: Balancing Control & Innovation Effective AI governance balances robust control with rapid innovation. By learning from the past, enterprises can design a resilient framework with the right guardrails, empowering teams to harness AI's full potential and keep pace with business. How does your Enterprise handle AI governance?

#ResponsibleAI is a major area of investment for John Snow Labs - you can’t call a #Healthcare #AI solution “state of the art” or “production ready” if it's doesn't work in a reliable, fair, transparent, secure, and transparent fashion. Some of the solutions out there today are outright illegal. We're active members of the Coalition for Health AI (CHAI) and I co-lead the fairness, equity, and bias mitigation workgroup. We also have a full team working on the #OpenSource #LangTest project, which now automated 98 types of tests for evaluating and comparing #LargeLanguageModels. If you're looking to learn more about this topic over the holiday, read the Responsible AI blog: https://lnkd.in/gPs8c2Yf Here are some of the areas this blog covers: * Unveiling Bias in Language Models: Gender, Race, Disability, and Socioeconomic Perspectives * Mitigating Gender-Occupational Stereotypes in AI: Evaluating Language Models with the Wino Bias Test * Testing for Demographic Bias in Clinical Treatment Plans Generated by Large Language Models * Evaluating Large Language Models on Gender-Occupational Stereotypes Using the Wino Bias Test * Unmasking Language Model Sensitivity in Negation and Toxicity Evaluations * Detecting and Evaluating Sycophancy Bias: An Analysis of LLM and AI Solutions * Evaluating Stereotype Bias with LangTest * Beyond Accuracy: Robustness Testing of Named Entity Recognition Models with LangTest * Elevate Your NLP Models with Automated Data Augmentation for Enhanced Performance   #ethicalai #ai #datascience #llms #llm #generativeai #healthcareai #healthai #privacy #security #transparency #softwaretesting

⛔ What Do I Not Know I Need to Know About ISO 42001 and the EU AI Act? We continue to read about the rapid evolution of AI governance, with frameworks like ISO 42001 and the EU AI Act setting new standards for responsible development and deployment. Organizations are understandably eager to navigate this complexity and achieve compliance, but what if there are hidden blind spots? One crucial yet often overlooked aspect is data provenance. Your obligations don't just involve having data privacy measures in place; the real onus is understanding the journey of your data - from collection to usage and deletion. ️ So, what do you not know you need to know about data provenance in the context of ISO 42001 and the EU AI Act? Here are some key questions to consider: ❓ Can you trace the origin of every piece of data used in your AI systems? This includes metadata like collection source, purpose, and modifications. ❓ Do you have mechanisms to track how data is used throughout its lifecycle within your AI systems? This includes understanding transformations, inferences, and outputs. ❓ Can you demonstrate compliance with data minimization principles? Are you collecting only the data truly necessary for your AI models? ❓ How do you ensure data quality and integrity throughout its journey? This includes measures to address bias, errors, and manipulation. ❓ Are you prepared to provide explanations for AI decisions, considering data provenance? This is crucial for transparency and accountability under both frameworks. Taking Action on Data Provenance: ✅ Conduct a data inventory: Map your data flows and identify all sources, uses, and storage locations. ✅ Implement data lineage tools: Automate tracking and recording of data movement and transformations. Enforce data governance policies: Establish clear guidelines for data collection, usage, and access. ✅ Integrate data quality checks: Regularly assess data for accuracy, completeness, and consistency. ✅ Develop explainable AI (XAI) solutions: Make data provenance a core component of your XAI strategy. Remember, data provenance is bigger than compliance; it's about building trust and ensuring responsible AI development. By proactively addressing these blind spots, you can confidently navigate the evolving regulatory landscape and unlock the full potential of AI for your organization. ⛔ So one more time - What Do I Not Know I Need to Know About ISO 42001 and the EU AI Act? If you have questions or need help working through the process, please don't hesitate to let us know. #AIgovernance #dataethics #ISO42001 #EUAIact #responsibleAI #dataprivacy #dataprotection #XAI #AItransparency #ALIGN #TheBusinessofCompliance #ComplianceAlignedtoYou

Are you interested in building a GenAI powered application that not only captivates audiences but also prioritizes Responsible AI? In the latest article, we explore the key strategies and best practices for creating a trustworthy and engaging GenAI application. From product planning to model selection, guardrails implementation to continuous monitoring, we cover it all! Discover how to: - Define clear Responsible AI goals - Select the right language model through rigorous experimentation - Implement robust input and output guard rails - Establish a comprehensive monitoring and auditing framework - Foster a culture of collaboration and continuous improvement Don't miss out on this comprehensive guide to building a responsible GenAI-powered application. #ResponsibleAI #GenAI #StoryWriting #EthicalAI #AIDevelopment #llm #artificialintelligence #ai

It's clear that we’re moving beyond the very early days of generative AI—we’re now in the midst of an exciting and game-changing technological evolution. As new AI applications emerge and scale, responsible AI has to scale right along with it. Yet, more half of the 756 business leaders we surveyed say that their company does not have a team dedicated to responsible AI. Here are the top four best practices I give executives looking to get started to put this theory into practice: 1. Put your people first and deepen your workforce’s understanding of generative AI. 2. Assess risk on a case by case basis and introduce guardrails such as rigorous testing. Always test with humans to ensure high confidence in the final results. 3. Iterate across the endless loop that is the AI life cycle. Deploy, fine tune, and keep improving. Remember, innovation is an ongoing process, not a one-time goal. 4. Test, test again, and then test again. Rigorous testing is the secret strategy behind every innovation. Finally, remember there is no one central guardian of responsible AI. While the commitment of organizations and business leaders is vital, this effort is a shared responsibility between tech companies, policymakers, community groups, scientists, and more. https://lnkd.in/gg8anUWn

Seeking to develop a common understanding of the #AI accountability ecosystem, the OECD - OCDE published last week: "Common Guideposts to Promote Interoperability in AI Risk Management." The document provides a high-level analysis of the commonalities and differences of leading AI risk management frameworks (#NIST, #ISO, #AIDA, #EU AIA). 🤖 According to the #OECD, providing accountability for trustworthy AI requires that actors leverage processes, indicators, #standards, certification schemes, auditing and other mechanisms to follow the following steps at each phase of the AI system lifecycle phases: (1) Plan & design (2) Collect & process data (3) Build & use the model (4) Verify & validate the model (5) Deploy (6) Operate & monitor the system This should be an iterative process where the findings and outputs of one #riskmanagement stage feed into the others. As part of responsible business conduct (#RBC) practices, however, the #OECD recommends that companies carry out #duediligence to identify and address any adverse impacts associated with their operations, their supply chains or other business relationships. Their Due Diligence Guidance for RBC includes six steps: (1) Embed RBC into company #policies and management systems, (2) Identify and assess adverse impacts in operations, #supplychains and business relationships, (3) Cease, prevent or mitigate adverse impacts, (4) Track implementation of efforts to address risk, (5) Communicate on due diligence efforts, and (6) Provide for or cooperate in remediation when appropriate. These steps are meant to be simultaneous and iterative, as due diligence is an ongoing, proactive and reactive process. Finally, the report concludes that to develop trustworthy #aritificialintelligence systems, there is a need to identify and treat AI risks. "This report demonstrates that while the order of the risk management steps, the target audience, scope and specific terminology sometimes differ, main risk management frameworks follow a similar and sometimes functionally equivalent risk management process. As governments, experts and other stakeholders increasingly call for the development of accountability mechanisms, . . . interoperability between burgeoning frameworks would be desirable to help increase efficiencies and reduce enforcement and compliance costs." https://lnkd.in/gtTZ2i77

It’s time to graduate from the AI kids table and take a seat at the adults' table, where the conversation turns to crafting a mature and secure AI-powered future. Here are some critical points to help leaders foster this growth: 𝟭. 𝗚𝗼𝗼𝗱 𝗗𝗮𝘁𝗮   Good data refers to the quality, accuracy, and relevance of the data used in AI systems. It's the cornerstone of any AI model, ensuring that the decisions made are based on reliable and appropriate information. To promote good data, leaders should invest in robust data management systems, emphasize the importance of data integrity, and encourage continuous data assessment and improvement practices within their teams. 𝟮. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 Responsible AI encompasses ethical considerations, transparency, fairness, and accountability in AI development and deployment. It's about creating AI that respects human values and societal norms. Leaders can advance responsible AI by establishing ethical guidelines for AI development, fostering a culture of transparency and fairness, and ensuring there are mechanisms in place for accountability and continuous ethical evaluation. 𝟯. 𝗦𝗮𝗳𝗲 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 Safe deployment involves the careful introduction and integration of AI systems into operational environments, ensuring they function as intended without causing unintended harm or disruption. To ensure safe deployment, leaders should prioritize rigorous testing and validation of AI systems, create protocols for monitoring AI performance in real-world settings, and establish responsive feedback mechanisms to quickly address any issues that arise. 𝗜𝗻 𝗮 𝗻𝘂𝘁𝘀𝗵𝗲𝗹𝗹: Transitioning from AI talk to mature AI action requires leaders to tirelessly champion the integration of high-quality data, uphold ethical AI practices, and rigorously enforce safe deployment protocols. As a leader, what innovative practices are you bringing to the table to boost your organization's approach from the basics to brilliance?