If you’re looking to launch a Tech for Good project using Generative AI, here are our Chief Design Officer Ray Clarke’s expert strategies and user-centric insights into some of the questions often asked in the process and how to address them.
1. What metrics or criteria could we use to evaluate the impact of AI based tech for good applications?
Metrics and Criteria’ to Evaluate the Impact of AI-based Tech for Good Applications.
When evaluating the impact of AI-based tech for good applications, it is essential to consider various metrics and criteria, particularly from the perspective of evolving best practices and user experience design.
Qualitative metrics focus on usability and accessibility. Measuring user satisfaction through surveys and feedback is crucial, as is assessing the learning curve and overall user-friendliness of the product. Ensuring accessibility for people with disabilities and cognitive needs is also a key consideration.
Quantitative metrics assess the impact on user uptake. This involves measuring how many people start using the application post-AI implementation and how quickly they achieve their desired outcomes with AI assistance.
Performance metrics focus on the effectiveness and efficiency of the AI system. Analysing the speed, accuracy, and reliability of the AI system is vital to ensure it meets performance standards.
Sustainability metrics consider the environmental impact of AI applications. Evaluating the ecological footprint of deploying and using the AI application is necessary to ensure environmental sustainability.
Continual feedback and community impact assessment. Involve regular Net Promoter Score benchmarking to measure user satisfaction and assess the broader economic, social, and cultural effects on the user community.
2. What are the most exciting technological advancements in AI that can contribute to social good?
In healthcare, AI diagnostics have significantly improved accuracy in detecting diseases through AI-powered imaging and predictive analytics. AI also enhances training by creating real-world scenarios for staff training at scale, such as immersive university training rigs for handling abusive or violent patients.
In education, adaptive learning platforms offer personalised learning experiences that adapt to students’ needs and progress.
In environment and sustainability, AI solutions optimise energy use in buildings, reduce emissions, and manage renewable energy sources. For instance, smart metering and recommending cost-effective solutions like grey-water usage are examples of AI contributions in the utility space.
In public safety, AI analyses data to predict and prevent criminal activities, enhancing community safety. Connected data can identify behaviours across CCTV and data without focusing on individual identification.
Comparing AI Advancements to Traditional Methods
AI solutions can be more cost-effective compared to human operatives, with increased accuracy and speed over manual tasks. Additionally, AI systems continually learn and improve through iterative modelling, enhancing user outcomes over time.
Limitations of Current AI Technologies
Several limitations hinder the effectiveness of AI technologies. Many organisations lack in-house AI expertise, and the high costs associated with AI tools and implementation can be prohibitive. Issues with data quality, availability, and privacy also pose significant challenges.
3. What advice would you give to new technologists who want to work on AI for Good projects?
New technologists should prioritise understanding user needs by conducting user research to grasp the challenges and context of target users. Developing deep empathy for users and stakeholders, encouraging inclusivity by working with diverse teams, and ensuring simplicity and intuitive interaction are crucial.
Effective communication is essential. Engage with communities to involve all end-user stakeholders in the development process, ensuring the technology meets real needs. Transparency about how AI systems work and the data they use is vital.
Measuring impact through continuous evaluation is necessary. Regularly assess the effectiveness and impact of the technology on users and be open to iterating, testing, and validating. Be adaptable and prepared to pivot based on feedback and changing needs.
4. How can we put AI in the hands of charities and social impact organisations, and what are the challenges?
To effectively implement AI in charities and social impact organisations, start by identifying specific user needs and goals that AI can address. Define clear, measurable objectives for AI implementation. Promote the use of open-source AI technologies that are freely accessible and encourage partnerships between tech companies and social impact organisations. Create platforms for resource sharing, including data, tools, and expertise.
Challenges to consider include resistance to change, as organisational or user resistance to adopting new technologies can be significant. Ensuring the long-term sustainability of AI initiatives is another challenge, with costs and funding potentially being prohibitive to long-term implementation.
If you’re interested in exploring Generative AI for your business, take a look at our AI services that allow you to quickly test and implement generative AI-based solutions into your business.
