What do you do if your AI projects lack the human touch?

I remember when I developed first chatbot we were able to measure correction, confidence, fallback & accuracy for an AI model. Most difficult to capture emotional while conversation. However, now AI enabled us to capture emotions using sentiment analysis where user emotions is captured based on words, sentence, phrases used while conversations. Using sentiment analysis we can give human touch to AI to recognize and respond with emotions. For each conversation we used to capture these emotions happy, sad, frustrated, angry, etc. Based on conversation with user AI bot/NLP recognizes and respond by empathetic response whenever required.

This is the kind of issue many companies will soon face in their race to "AI" their company. Name one ad made entirely by AI that has sold anything. You can't! If we remove the human in the loop, then it's just a loop - repetitive, circular, similar, unchanging. It's not always about adding more AI, but how to keep the humans.

Well, then, you bring in the needed human touch. People resonate with things they can easily connect to. Humanizing AI is an attempt to tap into a human-like response and connect with the feeling/emotional side of humans.

To humanize AI, integrate emotional AI capabilities, recognize, and analyse emotional cues, train AI for empathetic responses, and enhance user experience. Use sentiment analysis and machine learning for better emotional understanding, uphold privacy and ethical standards, refine emotional intelligence, and balance AI integration with human input, ensuring fairness and relatability.

Context is important here. Few considerations - where is AI being applied? To what extend? What problem is being solved? How does that impact humans? Are models interpretable easily? These are important design questions to consider as we think about humanizing AI. Let's say AI is leveraged for static "rule based question answering" to find the author of a book, humanizing AI might not be as relevant. However, if AI is applied for recruiting, it becomes imperative that AI systems are vetted to ensure they are emotionally intelligent and ethical. For example, how does AI handle career gaps, gender, race, age, career transitions? This cannot be simply answered by looking at model performance. Where and when to humanize AI is as important !

If you want AI to act more human like, work with a range of people with different personalities. - try adding tone and personality as part of your prompt based on guidance from people that are not coders Take a break before trying to add a persona to your AI - Anyone that has coded for a while understands our brains start to think differently when coding. Explore different approaches - I’ve included explicit tone and personality traits as system prompts for LLMs, example of responses, and pages of context - some work some don’t

To design a good product, the artificial intelligence team should form a cross team with the business team from the beginning

In addition, there's another significant factor that enhances trust in AI: user participation. People contributing to AI projects provide a sense of ownership and trust. This principle, often seen in open-source projects, applies equally to AI. By allowing users to contribute, we're not only enriching the AI with a broader range of insights and data but also building a community that trusts and supports the AI because they see their input reflected in the outcomes.

A cross discipline team is such an important factor in developing human centered AI solutions. But the team makeup is just the first step. Team effectiveness goes beyond diversity and unique perspectives in the makeup of the team. To truly be human centered, team goals and measures need to incentivize continuous learning and responsible decision making in a sustainable pace. Reframe underlying strategies to deliver beneficial, not just usable solutions.

Don't limit yourself to just tech experts when developing your AI project. Involve people from different backgrounds, such as designers, writers, and psychologists. This will ensure that your AI is not only functional but also user-friendly and engaging. For example, a team developing an AI assistant for booking travel reservations could include a programmer to handle the technical aspects, a travel agent to provide insights and ensure the AI suggests realistic and relevant destinations and activities, and a user interface (UI) designer to create an interface that is intuitive and enjoyable to use.

Implement a 'storytelling' feedback mechanism where users share their experiences with the AI in narrative form. This approach can reveal the user's emotional journey, providing deeper insights into the AI’s impact on their lives. Analyzing stories rather than cold data can unveil nuanced feedback on user interaction, making your AI more adept at understanding and responding to the human element.

Get real people to test out your AI and see how they respond to it. Their feedback will help you identify areas where your AI can be improved to make it more human-like. For instance, you could conduct usability testing sessions where users interact with your AI and provide feedback on their experience. Ask them questions like “How comfortable did you feel interacting with the AI?” or “Did the AI seem like it understood your requests?”

An exmaple of how this can be acheived is RLHF, that is reinforcement learning from human feedback. Implementing such algorithms will allow us to collect feedback from users and again use this data to AI for better performance. However, such methods need to be used with a certain amount of caution as high amount of training might lead to poor performance of the AI system itself.

User feedback is key to creating a human and ethical AI solution. Have a "humans @the center" approach. In early stages of your AI journey, make sure to consider and include the PoV of your target audience. However don't do it just in the beginning of your journey, but do it continuously, while developing but also once you hit the market!

Actively involve users in the development process through continuous feedback loops. Utilize surveys, interviews, and user testing sessions to gather insights into how people interact with your AI system and how it affects their lives. This feedback is invaluable for identifying areas where the human touch is lacking and for understanding the emotional and practical impact of your AI solution on users. Iteratively refining your project based on this feedback ensures that your AI solutions are not only technically advanced but also deeply connected to the human experience.

Here are the points that give a brief idea about Ethical Considerations: - Prioritize ethical considerations when AI projects lack the human touch. - Respect user privacy to prevent manipulation or intrusion. - Ensure emotional analysis and personalization don't cross boundaries. - Establish clear ethical guidelines to maintain user trust. - Promote a positive user experience by being transparent about data usage. - Provide users with control over their interaction with AI.

As AI becomes more sophisticated, it's important to consider the ethical implications. Make sure your AI is designed and used in a way that is fair, unbiased, and respectful of people's privacy. For example, avoid creating AI that discriminates against certain groups of people or that collects and uses user data without their consent.

Quick and dirty - an advice received from one of the AI pioneers Andrew Ng > set up diverse teams. This is the best way to have ethics included in your AI approach/ project.

it is ethically imperative that the end user is clearly aware when interacting with AI-generated content, ensuring there is no confusion or misleading perceptions of engaging with human generated content. Transparency about the AI's role is crucial to uphold trust and integrity in user interactions.

Establishing ethical guidelines for your AI projects is an important tool to build trust Such guidelines might include : - transparency about how data is used - transparency about the use of an AI (it shouldn’t be hidden) - users information - users control Don’t forget to advertise those guidelines !

Customer experience is the heart of digital transformation, the more personalized this experience is, it will have a big change in user usage and attract audience.

To infuse your AI projects with a human touch, start by integrating user feedback loops, ensuring the technology addresses real human needs and preferences. Emphasize ethical design and empathy in AI development, focusing on inclusivity and accessibility. Collaborate with interdisciplinary teams, including psychologists and sociologists, to understand and implement human-centric principles better. Remember, the goal of AI is to augment human experiences, so keeping the end-user at the center of your project will naturally bring a more human touch to your work.

If my AI project lacks human touch, I focus on incorporating personalized experiences. This involves understanding user preferences, behaviors, and needs to tailor interactions accordingly. By leveraging data analytics and machine learning algorithms, we can create personalized recommendations, responses, and interfaces that resonate with users on a human level. Additionally, soliciting user feedback and iteratively refining the AI system based on these insights further enhances its ability to provide meaningful and engaging experiences.

In the landscape of AI, where algorithms churn vast data seas, it's the personalized experiences that truly anchor the human touch. My journey as an AI/ML Consultant has taught me the profound impact of tailoring AI to resonate with individual user narratives. This isn't just about user interfaces or recommendations; it's about crafting AI systems that adapt, learn, and grow with each user, creating a symbiotic relationship where both AI and user evolve through interaction. Personalization isn't a feature; it's the very ethos of human-centered AI.

People appreciate feeling like they're interacting with a real person, not a machine. Personalize the way your AI interacts with users by tailoring its responses to their individual needs and preferences. For instance, an AI shopping assistant could recommend products based on a user's past purchases and browsing history, or use information from a user profile to suggest items that fit their style or interests.

When AI projects lack a human touch, I focus on continuous learning. By designing AI for ongoing improvement, it adapts to user interactions, becoming more attuned to their needs. Implementing machine learning models that learn from each interaction ensures AI remains relevant and relatable, enhancing its human-like qualities over time.

Introduce an 'AI evolution log' where the system records its learning journey, sharing insights and changes with users. This transparency can foster a sense of growth and partnership between the AI and the user, highlighting how the system adapts and evolves in response to interactions, making the learning process a shared experience.

I've always found it helpful to engage with the AI after a work session or project, and I asked that it provide a rough overview of the instructions it followed or utilized to complete what I requested it do. I often find the AI has followed my instructions well but can better outline a more suitable or cleaner path to get there. Continuous learning is great for the AI, but we, as prompt engineers or enablers, must also remain open to learning from the AI in return.

Incorporate 'AI mindfulness sessions' where the AI prompts users to reflect on their thoughts and emotions, encouraging mindfulness and self-awareness. By integrating these reflective sessions, your AI can contribute to users' mental and emotional well-being, adding a new dimension to the human-AI interaction that goes beyond functionality to foster a deeper human connection.

Even though the "human touch" will improve the ergonomics of human-machine interaction, it might be interesting to explore the trade-offs. For example, a study showed that people who gave machines human characteristics (e.g. name, gender, etc.) cared less about the machines' errors.

Consider the psychological impact of AI interactions and strive to create experiences that are emotionally intelligent and culturally aware. AI should not only understand commands but also the emotional context behind them, making interactions feel more natural and human-like.

In sectors where empathy, nuanced understanding, and personal service are paramount, prioritize integrating human oversight, personalized communication, and empathetic design elements into your AI solutions in critical. For industries less dependent on these factors, focus on enhancing efficiency and accuracy. Tailor your approach to the unique demands and sensitivities of each sector to ensure your AI project aligns with end-user expectations and needs.

To incorporate more human touch into AI projects, we have to focus on how technology interacts with and enhances human experiences. The key is to find the right balance between technical excellence and empathetic, user-centric design. Here are some ideas to infuse more of a human touch into your AI projects 1) understand your audience through research 2) focus on human-centered and intuitive design experiences 3) incorporate conversational and voice capabilities into your AI products 4) use storytelling to make complex information relatable 5) incorporate human oversight via methodologies like Human-in-the-Loop (HITL) and feedback loops and 6) build community and user engagement and ecosystem around your AI products and platform