TECHNOLOGY & CULTURE
Human flourishing in the Age of AI
Challenges, strategies, & opportunities.
The explosion of AI over the past two years, particularly generative AI and large language models (LLMs), has reshaped much of how we work and think about technology. For user researchers and designers, AI’s impact can be grouped into three broad areas:
- Generative AI product development: The topics and challenges explored in research and design projects as we seek to best implement AI into new products and services.
- Internal processes: Systems and workflows within the workplace leveraging AI to enhance efficiency and insight as we work together to build AI-leveraged products and services.
- Personal practices and training: Individual use of AI to augment skills and productivity.
While AI offers immense potential to accelerate and enhance product design, it’s critical to approach it with a balanced perspective. Alongside its benefits, AI presents potential harms and negative impacts that demand careful consideration.
As this transition is happening, many of us are asking ourselves how we can approach AI in a human-centered way. Some of the key questions to guide responsible AI integration include:
- How can organizations advocate for the development of AI technologies that prioritize human well-being in their products and services?
- What strategies ensure AI can be leveraged responsibly while centering human needs during research, analysis, design, and the software development process?
- How can authentic human experiences be validated within a landscape increasingly shaped by artificial content?
- What day-to-day practices can researchers and designers adopt to maintain a balanced, human-centered approach to AI?
And most importantly:
How can researchers and designers learn, grow, and adapt while AI technology is evolving faster and faster?
In this article, I will attempt to answer these questions by revisiting foundational concepts of human flourishing, reflecting on organizational values, and synthesizing diverse perspectives from professional communities and academic literature.
The goal is to help us all develop best practices for AI use that align with both ethical standards and business goals.
And keep the human centered in the loop.
A human-first approach
Adopting a human-first approach provides a strong foundation for ethical and effective AI use. At its core, this philosophy emphasizes serving and empowering people through empathy, authenticity, and a commitment to collective well-being.
I think of Human-First as humans serving humans. Every decision, every interaction is grounded in empathy, authenticity, and the acknowledgment of our collective humanity. We take care of ourselves and keep the health of others in mind. We create space when needed.
Taking the core concepts of human flourishing as identified by academics and applied practitioners, (See Appendix, below, for complete list of frameworks) organizations that embrace a human-first mindset define their values around these core principles:
- Purpose and Contribution: Supporting work that feels meaningful and impactful.
- Personal Growth and Agency: Encouraging self-determination and skill development.
- Holistic Well-Being: Addressing physical, mental, social, and other dimensions of health.
- Ethical Living: Ensuring actions align with moral values and promote harmony.
A holistic view of human flourishing considers the individual, structural, systemic, and environmental levels to create sustainable, people-centered solutions.
Interdisciplinary and cross-cultural frameworks (e.g., the Social Ecological Framework, The Ecology of Wellbeing, & Measuring Flourishing | Harvard), can be applied to aide us in decision-making.
By rooting AI practices in these values, researchers, designers, and organizations can better navigate challenges to human flourishing in research and design. This foundation sets the stage for addressing specific AI-related concerns while advancing the shared goal of creating technologies that truly serve humanity.
It is in this context that we next identify AI-related challenges to human flourishing in the context of UX Research and Design.
AI challenges to human flourishing and lessons learned
The more I experiment with AI tools while also conducting research and consulting with companies building generative AI-based tools, the more I can see the limitations of how AI can detract from human flourishing.
While societal concerns surrounding AI are vast and complex, (See AI Risks in Appendix, below), this discussion focuses on challenges most relevant to day-to-day experience of user experience (UX) and design research work.
Through personal (and team) experience and reviewing articles and perspectives, we have identified four key characteristics of AI as particularly threatening to human flourishing:
1. Oversimplification
AI’s limited ability to detect and adapt to complex, changing contexts can lead to oversimplification of human behavior and realities, culturally or situationally inappropriate insights and output, and perpetuation of systemic inequities and injustices.
Frequent users of AI tools have likely had an experience with AI output that isn’t quite what you were looking for. In those moments, it feels like AI isn’t gleaning the intent behind your question. Sometimes even the most well-crafted prompt isn’t enough to overcome this barrier.
AI models often struggle to fully understand and adapt to contextual nuances, particularly in complex or dynamic environments where human interpretation is key. It falls short in fully grasping the subtleties of human communication, including tone, cultural references, and implied meanings, as well as sussing out potential motivations or explanations for human behavior and phenomena (What Are the Limitations of AI in Understanding Context in Text? — Space Coast Daily & The Context Problem in Artificial Intelligence — Communications of the ACM).
As it relies on predefined rules and training data, it can fail to factor in social, cultural, systemic, and environmental influences out of purview.
These characteristics limit AI’s responsible utilization in research across contexts, cultures, and in novel situations, and when completing tasks that require high-stakes or creative problem-solving.
Research and design often involve making sense of a complex interplay of user and contextual factors that combine to drive behavior or shape an experience — and it seems that AI tools are not yet advanced enough to appropriately capture and process this complexity.
Not all generative AI tools are the same and having awareness about each product’s context windows ensures we’re realistic about to what extent the tool can help us center humanness in research and design (What is a context window?).
2. Propensity to generalize
AI’s tendency to summarize and generalize poses a risk to the representation of diverse human experiences and inclusivity. While AI excels at processing large datasets to identify common patterns and deliver efficient summaries, this strength can become a limitation when it oversimplifies nuanced perspectives or excludes less common experiences.
For example, AI-powered search engines highlight the most popular answers but may exclude context-specific insights, leading to incomplete or biased conclusions (The AI Summarization Dilemma: When Good Enough Isn’t Enough: Center for Advancing Safety of Machine Intelligence — Northwestern University & AI’s dark secret: It’s rolling back progress on equality | Context).
Similarly, relying solely on AI for research analysis can result in a surface-level understanding of the average. AI analysis may lead to the full spectrum of participant responses not being considered, ignoring minority perspectives, and creating exclusive products or experiences.
There are times in analysis where you want to understand the broad themes — but there are also times where it's important to understand individual nuances.
These limitations are particularly dangerous when researching diverse populations or designing solutions that require high degrees of sensitivity and nuance. Indeed, as we’ve experimented with AI tools for research and analysis, we have found outputs to be inadequate and potentially misleading, and we find ourselves needing to reintegrate human subtleties and dive deeper into oversimplified insights.
3. Lack of transparency
AI tools’ lack of transparency and data privacy guardrails can infringe on our basic human right to privacy and decrease our sense of agency to choose our relationship with technology.
Despite efforts to improve transparency and develop privacy-centric AI, using AI often still feels like working with a “black box” — with users still missing deep understanding of how AI processes their data and clear, succinct explanations of privacy practices (We Must Fix the Lack of Transparency Around the Data Used to Train Foundation Models · Special Issue 5: Grappling With the Generative AI Revolution & Transparency is sorely lacking amid growing AI interest | ZDNET).
This has implications as we use AI to collect and analyze data from people and as we try to develop AI-powered products that promote consent, agency, and empowerment.
As researchers and designers, we have a duty to protect personally identifiable information (PII) of research participants and the intellectual property of our clients.
We have a responsibility to ensure that our research participants have the power to consent to how their data is used and to our consumers to create products and experiences that do not lead to privacy breaches and data exploitation. In our experience, when using popular AI tools to their full functionality, we cannot guarantee those protections will be upheld.
4. AI isn't aware of its' own bias
AI’s tendency to reproduce bias and generate inaccurate output can exacerbate existing social inequalities and creates threats to informed decision-making.
For example, the UK passport photo checker showed bias towards women and darker-skinned people:
The tendency for AI tools to perpetuate and exacerbate human biases present in their training data is probably the most commonly discussed threat of AI, so we won’t discuss this issue in depth here (Battling Bias in AI).
Bias can lead to discriminatory experiences for research participants, skewed insights, narrowed scope of potential design directions, and designs that cater to hegemonic identities and majority user groups.
Hallucinations: Beyond biased output, there is the potential for hallucinations which produce nonexistent or inaccurate outputs (When AI Gets It Wrong: Addressing AI Hallucinations and Bias — MIT Sloan Teaching & Learning Technologies). This misinformation could affect research and product decisions in major ways.
In another example, Air Canada's chatbot lied to a passenger about bereavement fares but the customer later won the case:
The passenger claimed to have been misled on the airline’s rules for bereavement fares when the chatbot hallucinated an answer inconsistent with airline policy. The Tribunal in Canada’s small claims court found the passenger was right and awarded them $812.02 in damages and court fees… the court found Air Canada failed to explain why the passenger should not trust information provided on its website by its chatbot. Source: Forbes
While being aware that generative AI products can produced biased or inaccurate information is a good first step, we feel there is still unmet need for transparency and diversification of training datasets and extensive training on critical evaluation of AI output. AI must be leveraged judiciously and always in service of human-centered needs.
Addressing threats to human flourishing
As we advance our use of AI, we must remain committed to prioritizing the human experience and fostering the well-being of our colleagues, research participants, clients, and the customers who use the products we help create.
Technology should contribute to the well-being, growth, and fulfillment of people and their communities.
Addressing the four challenges discussed above, here are four strategies to combat the limitations.
Strategy #1: AI as a complement, not a replacement
AI has proven to be a powerful tool in research, but its greatest potential lies in complementing, not replacing, human expertise. Understanding where AI excels and where humans bring unique value allows us to strike the right balance.
Where AI shines:
- Processing large data sets: AI’s computational power allows it to analyze vast amounts of data far faster than humans, making it an indispensable tool for pattern recognition and large-scale analysis.
- Generating initial ideas: AI is excellent at sparking brainstorming by presenting diverse, unbiased possibilities, which can help overcome creative blocks.
- Recognizing patterns: AI’s pattern-recognition capabilities are unmatched for identifying trends and correlations across datasets.
Where humans shine:
- Empathy and connection: These are foundational to qualitative research. Building trust, reading body language, and engaging authentically are uniquely human abilities that technology cannot replicate.
- Understanding complex contexts: Humans excel at synthesizing subtle, multifaceted information that may not fit neatly into patterns.
- Ethical and contextual judgment: Humans bring cultural and moral considerations into decision-making, ensuring sensitivity and appropriateness.
- Unique insights: The creativity and contextual understanding required for truly novel insights remain human strengths.
Striking the right balance
Data collection: AI can enhance efficiency in data collection when used intentionally. For example, it can assist with participant screening during recruitment, but researcher oversight ensures quality and appropriateness. Human moderation remains indispensable for creating connection, fostering empathy, and understanding participants deeply.
While AI moderation is effective for executing qualitative research quickly and at scale (Accelerating Research with AI), it cannot replicate the depth of human engagement.
Data analysis: In analysis, AI can be valuable for identifying major themes and aiding qualitative data coding, providing researchers with a head start. Transcriptions alone are a great start. When it comes to summaries, most tools I've tried are ok at this, but the future promise is there.
However, interpreting participant behavior, understanding nuances in communication, and recognizing diverse perspectives still greatly rely on human expertise. AI serves as a tool for initial synthesis and as a point of comparison, but humans are indispensable in making sense of the human experience.
Data generation: Using AI to generate qualitative data, such as having AI simulate human responses, can jeopardize the integrity of research by misrepresenting authentic experiences.
That said, there are cases where AI-generated responses can enhance research outcomes. For instance, immersive AI avatars have been used effectively in healthcare provider (HCP) market research to elevate engagement and provide richer insights, offering a viable alternative in specific contexts (How we elevated HCP market research engagement and insights using AI avatars for an immersive experience — Research Partnership).
By leveraging AI as a complement to human expertise, we can enhance efficiency and scalability without compromising the depth and integrity of research. The key is intentionality — using AI where it excels while relying on human strengths to truly understand and connect with people.
Strategy #2: Contextually and culturally-aware implementation
Human diversity is central to effective cross-cultural research and design, and understanding the differences between individuals, their daily contexts, and broader sociocultural environments is key to generating meaningful insights. This same principle applies to the thoughtful integration of AI into practices and workflows.
AI implementation should be deliberate and context-sensitive, with careful consideration of when and how AI is the right tool for the task. Context plays a pivotal role in determining whether AI enhances or detracts from the goals of a given project. Tailoring AI strategies to align with cultural nuances, environmental factors, and user needs ensures that technology complements, rather than complicates, the work at hand. For example:
- Building rapport: If establishing trust and encouraging participants to open up about sensitive topics is essential, AI may not be the best fit.
- Anonymity preferences: In contrast, participants may prefer the perceived neutrality and anonymity of an AI moderator when discussing highly personal or taboo subjects.
- Cultural perceptions: In Western Europe, heightened concerns about AI and data privacy influence how AI is received and used, requiring careful consideration of tools and methods (Will the EU AI Act work? Lessons learned from past legislative initiatives, future challenges | IAPP & How concerned are Europeans about their personal data online? | European Union Agency for Fundamental Rights).
- Social dynamics: In Brazil, where authentic social connections are highly valued, human-to-human interaction may be preferred for meaningful engagement (How to Apply Cultural Knowledge in Your Brazilian Localization Strategy).
- Research goals: For tactical questions or high-level sentiment analysis, AI can effectively identify trends and major pain points. For deeper explorations of complex motivations or mental models, human-led research is often more appropriate.
When implementing AI, it’s essential to stay well-informed about the tool’s capabilities and limitations, including its context windows and potential blind spots. Organizations designing AI products should prioritize localization and enhanced context sensitivity to ensure these tools address diverse human needs effectively.
By thoughtfully balancing human expertise with AI-driven methods, it’s possible to create solutions that honor cultural uniqueness while leveraging technology to deepen understanding and foster meaningful connections.
Strategy #3: Privacy and consent practices
Effective AI implementation requires balancing innovation with robust privacy and consent practices. Popular AI platforms often retain data input to train their tools, raising concerns about confidentiality and data security.
Zoom subtly updated their terms of service in March, 2023, leading to a backlash and then backpedaling and clarification in August:
To address these risks, organizations should establish clear policies to safeguard sensitive information, including personally identifiable information (PII) and proprietary data (Can GPT-4o Be Trusted With Your Private Data? | WIRED). These should be shared openly and in advance. Practices like anonymization and secure data storage can help minimize risks from the outset. For organizations seeking greater control, developing proprietary AI models is an option worth exploring.
Transparency is a cornerstone of effective privacy and consent practices. Providing research participants with detailed information about AI use in consent forms and participation materials enables them to make fully informed decisions about how their data is handled. Encouraging team members to share questions or concerns about AI tools fosters a culture of open dialogue and ethical accountability, ensuring that privacy practices stay aligned with both internal values and external expectations.
Additionally, applying user experience (UX) and human-centered design principles to AI technologies can make privacy and security features more transparent, accessible, and empowering. This ensures that consent goes beyond a checkbox to become a meaningful and informed part of the user experience (The AI Consent Conundrum: Do We Truly Understand What We Agree To? | by Neria Sebastien, EdD | Medium).
By adopting these strategies, organizations can align their AI practices with both ethical standards and user expectations, creating tools and systems that promote trust and human flourishing.
Strategy #4: Ongoing AI training & discussion
As AI evolves rapidly, staying informed, critically evaluating its capabilities, and understanding its impact are essential for leveraging its full potential. A team-based approach to AI training encourages shared learning and open discussions about its possibilities and limitations. This not only helps refine policies and address concerns but also fosters innovation as the technology progresses.
Effective AI strategies involve tackling key topics such as maintaining non-disclosure and data privacy requirements while using AI, reviewing outputs to identify and mitigate bias or misinformation, and finding ways to enhance efficiency and effectiveness. These conversations are vital for ensuring that AI is used responsibly and productively.
A human-first philosophy should guide these efforts.
Organizations should regularly assess AI’s impact not only on participants, consumers, and clients but also on internal teams.
The aim is to ensure AI supports meaningful work — allowing people to build new skills, refine creative and critical thinking, and stay engaged in tasks that are both purposeful and impactful. AI should empower teams to feel more efficient and effective while safeguarding their sense of purpose (Finding Meaningful Work in the Age of AI | LinkedIn).
AI training and policies must remain flexible and adaptable. As technology evolves or reveals limitations, organizations should be prepared to recalibrate their approach, ensuring that human values remain at the center of innovation. By embracing this mindset, businesses can harness AI’s potential while ensuring it serves people first.
AI and opportunities to promote flourishing
While this article has primarily focused on the ways AI challenges human flourishing and the strategies we, as researchers and designers, use to mitigate these risks, it’s equally important to recognize AI’s potential to promote flourishing.
When developed and applied with the specific aim of enhancing human lives, AI can paradoxically address even those areas where it poses the greatest risks, transforming them into opportunities for growth and well-being. Here are a few ways we’re excited about AI contributing to human flourishing:
Inclusive and accessible products: AI has the power to make products more inclusive and accessible by collaborating with diverse users and understanding their needs. When designed thoughtfully, AI can personalize experiences to adapt to individual abilities, preferences, and identities (How Artificial General Intelligence Could Redefine Accessibility).
For instance, AI-powered voice assistants can be trained to recognize diverse speech patterns, accents, and variations, breaking down communication barriers and fostering a sense of belonging for all users (Voice-activated Devices: AI’s Epic Role in Speech Recognition).
Automating low-level tasks and assisting with complex ones: AI can strategically automate repetitive and unfulfilling tasks, freeing people to focus on creative, meaningful, or strategic activities. By reducing human error and alleviating mental and physical stress, AI helps protect our sense of purpose and enhances productivity (The Ultimate Guide To Using (or Avoiding) AI At Work).
Conversely, AI can also act as a creative assistant for more complex, cognitively demanding tasks, such as brainstorming, design, writing, and art creation. By broadening our thinking and inspiring new possibilities, AI supports higher-level cognitive work and innovation (Creativity was another of ChatGPT’s conquests. Here’s why it’s more computable than we think. | by Paul Pallaghy, PhD | Medium).
Insights for positive behavior change: AI-powered analytics can identify patterns in behavior and generate actionable insights to encourage positive changes. For example, these insights can help improve products designed for health and education, empowering individuals to achieve their goals more effectively and efficiently.
- How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review — Mayo Clinic Proceedings: Digital Health
- CSRWire — A Bridge to Success: Using AI To Raise the Bar in Special Education
Enhanced data privacy and security: AI has the potential to improve data privacy and security through advanced capabilities such as anomaly detection, encryption, and access control management. Technologies like differential privacy and federated learning allow for valuable insights to be drawn from data while maintaining safeguards to protect sensitive information. These tools, when implemented conscientiously, can create systems that prioritize the privacy and security of research participants and clients.
- Generative AI & Data Security: 5 Ways to Boost Cybersecurity | BigID
- Are Data Privacy And Generative AI Mutually Exclusive?
- What is federated learning? — IBM Research
However, it’s important to acknowledge the inherent risks and challenges. The data-hungry nature of AI training often incentivizes excessive data collection, which can conflict with privacy objectives. Additionally, the complexity of AI systems sometimes makes it difficult to ensure that privacy protections are upheld consistently across applications. As a result, the risks associated with AI’s use in privacy-sensitive contexts often outweigh the potential benefits unless organizations approach implementation with exceptional care and transparency.
This dual perspective highlights the need for cautious optimism. While AI can enhance privacy in theory, realizing these benefits in practice requires prioritizing ethical design, robust regulation, and a commitment to limiting data use to what is strictly necessary. By balancing these considerations, organizations can mitigate risks and responsibly explore AI’s potential for improving data security.
Checking bias: AI can act as a “gut check” or an additional data point to help illuminate biases or blind spots in human decision-making when it is developed to be inclusive and address bias from the start. When trained on diverse datasets, AI tools can provide thoughtful recommendations, offering value in contexts ranging from product development to broader decision-making processes.
- Can the Bias in Algorithms Help Us See Our Own? | The Brink | Boston University
- How AI can end bias | SAP
Bridging cultural divides: While AI still has a long way to go in context sensitivity, its capabilities in real-time language translation and diverse content promotion are already helping bridge cultural and community barriers. For example, AI can enable more inclusive international research and create richer digital experiences that celebrate global diversity.
By intentionally designing AI to prioritize accessibility, security, and cultural sensitivity, we can harness its immense potential to foster connection, creativity, and well-being, ultimately driving human flourishing in ways that matter most.
- Bridging Cultural Divides: AI in Global Content Strategy | by Phan Nython | Medium
- The Role of AI in Bridging Cultural Gaps within Remote Teams
- Build Cross-Cultural Bridges, Not Barriers, With AI
Concluding thoughts & next steps
AI is a moving target, evolving rapidly in ways that challenge and inspire. As researchers, designers, and technologists, we have a unique responsibility to approach AI critically — assessing how it both promotes and threatens human flourishing. With regulation, governance, and accountability structures still taking shape, our vigilance and ethical commitment are more important than ever.
To ensure AI enhances rather than detracts from human flourishing, here are a few actionable steps:
- Apply a human-first lens: Continuously evaluate how AI tools align with values like inclusivity, transparency, and ethical responsibility.
- Balance AI with human expertise: Leverage AI’s strengths while retaining the depth, empathy, and nuance that only humans can bring. You may have heard of this as keeping a, "Human in the loop."
- Foster open dialogue: Share learnings and raise concerns within your teams and professional communities to shape better practices collectively.
- Explore the resources and appendix: Dig deeper into the resources referenced throughout this article and the extensive Appendix that follows to expand your understanding and spark new ideas.
- Advocate for responsible AI: Push for thoughtful regulation and design that centers human well-being at every level.
- Engage in conversation: Talk to your colleagues and friends. Talk to your manager. Talk to your clients. You can even talk to me. Whether you’re seeking practical insights, curious about integrating these strategies, or just exploring the topic in a collaborative way… conversing with others will bring these ideas to the forefront and keep us all moving forward in a human-centered way.
As researchers and designers shaping the products billions of people use daily, we hold the power to keep humans at the heart of this technology.
By being intentional, we can ensure AI evolves into a force that uplifts and empowers, rather than one that diminishes or divides.
Follow-up article: The Human-Centered AI Manifesto. Based on content and ideas from this article, the Manifesto breaks out principles of Human-Centered AI, Commitments to Action, and a link to a petition you can sign to signal your commitment to fostering Human-Centered AI.
Huge thank you to my colleagues Katie Trocin and LaToya Tufts for the lit review, content development, editing, and discussion that lead to the creation of this article.
Josh LaMar is the Co-Founder and CEO of Amplinate, an international agency focusing on cross-cultural Research & Design, based in the USA, France, Brazil, and India. As the Chief Strategy Officer of JoshLaMar Consult, he helps Entrepreneurs grow their business through ethical competitive advantage.
Appendix: References and Resources
Human Flourishing Frameworks
- Social Ecological Framework
- The Ecology of Wellbeing
- Measuring Flourishing | Harvard
- Authentic Happiness | Penn
- Philosophies of Happiness
- On the promotion of human flourishing | PNAS
- Rethinking flourishing: Critical insights and qualitative perspectives from the U.S. Midwest — PMC (nih.gov)
- Measures of Community Well-Being: a Template (springer.com)
- Flourish: A Visionary New Understanding of Happiness and Well-being
- Radically Human Technology: Enhancing Connection and Wellbeing (Or Finding your Ikigai Kairos ) | by Nichol Bradford | Transformative Technology | Medium
- THE 17 GOALS | Sustainable Development (un.org)
- Universal Declaration of Human Rights — Amnesty International
- Ayurveda’s Edge Over Western Psychology (bwwellbeingworld.com)
AI Risks
Limitations of AI
- What Are the Limitations of AI in Understanding Context in Text? — Space Coast Daily
- The Context Problem in Artificial Intelligence — Communications of the ACM
- What is a context window?
- The AI Summarization Dilemma: When Good Enough Isn’t Enough: Center for Advancing Safety of Machine Intelligence — Northwestern University
- AI’s dark secret: It’s rolling back progress on equality | Context
- We Must Fix the Lack of Transparency Around the Data Used to Train Foundation Models · Special Issue 5: Grappling With the Generative AI Revolution
- Transparency is sorely lacking amid growing AI interest | ZDNET
Bias in AI
- Battling Bias in AI
- When AI Gets It Wrong: Addressing AI Hallucinations and Bias — MIT Sloan Teaching & Learning Technologies
- There’s More to AI Bias Than Biased Data, NIST Report Highlights | NIST
- Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI (hbr.org)
- Can the Bias in Algorithms Help Us See Our Own? | The Brink | Boston University
- How AI can end bias | SAP
Strategy 1: Complement
- Accelerating Research with AI | NN/g
- How we elevated HCP market research engagement and insights using AI avatars for an immersive experience — Research Partnership
Strategy 2: Contextually Aware
- Will the EU AI Act work? Lessons learned from past legislative initiatives, future challenges | IAPP
- How concerned are Europeans about their personal data online? | European Union Agency for Fundamental Rights
- How to Apply Cultural Knowledge in Your Brazilian Localization Strategy
Strategy 3: Privacy & Consent
- Can GPT-4o Be Trusted With Your Private Data? | WIRED
- The AI Consent Conundrum: Do We Truly Understand What We Agree To? | by Neria Sebastien, EdD | Medium
Strategy 4: Ongoing Training
Opportunities
- How Artificial General Intelligence Could Redefine Accessibility
- Voice-activated Devices: AI’s Epic Role in Speech Recognition
- The Ultimate Guide To Using (or Avoiding) AI At Work
- Creativity was another of ChatGPT’s conquests. Here’s why it’s more computable than we think. | by Paul Pallaghy, PhD | Medium
- How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review — Mayo Clinic Proceedings: Digital Health
- CSRWire — A Bridge to Success: Using AI To Raise the Bar in Special Education
- Generative AI & Data Security: 5 Ways to Boost Cybersecurity | BigID
- Are Data Privacy And Generative AI Mutually Exclusive?
- What is federated learning? — IBM Research
- Can the Bias in Algorithms Help Us See Our Own? | The Brink | Boston University
- How AI can end bias | SAP
- Bridging Cultural Divides: AI in Global Content Strategy | by Phan Nython | Medium
- The Role of AI in Bridging Cultural Gaps within Remote Teams
- Build Cross-Cultural Bridges, Not Barriers, With AI
