EvalSDGs Week: Transforming SDG Evaluations with Artificial Intelligence: Insights from A Young and Emerging Evaluator by Lydiane Mbia

Lydiane Mbia

I am Lydiane Mbia Independent Research and Evaluation Consultant focusing on integrating technology into evaluation methodologies, and Board member of AGDEN. As a young and emerging evaluator working within intergenerational evaluation teams at SWEO and UNICEF within the past three years have shaped my evaluative thinking and perspectives on using Artificial Intelligence (AI) in evaluating Sustainable Development Goals (SDGs). Engaging myself in evaluation as a key informant and co-decision maker, I’m delighted to share how AI is becoming a transformative tool in our field, based on my experiences and extensive learning exemplified from AIDA | Origins: how the journey into artificial intelligence for development began. How then to leverage AI driven evaluation?

Harnessing AI in Evaluation: A New Era of Opportunities 

In my journey as an evaluator, complex data and challenging environments are common. Embracing AI has been exhilarating, as it revolutionizes data collection, analysis, and predictive analytics, and above all, it’s reshaping how we approach evaluations A Just Transition: What does it mean for AI and Evaluation?

Hot Tips

AI in the Planning Phase

  1. Set AI-compatible objectives: I align my evaluation objectives with AI’s capabilities, ensuring goals like data-driven predictions are achievable with AI tools.
  2. Choose the right AI tools: I select AI tools based on their suitability for evaluation, focusing on user-friendliness, scalability, and data security.
  3. Integrate AI into frameworks: I incorporate AI from the start, rethinking traditional methods to include AI-driven strategies for data collection and analysis.

AI in Data Collection: A Paradigm Shift

AI Tools with natural language processing (NLP) facilitate in extracting insights from vast, unstructured data sets, making the evaluation process more efficient and thorough.

Data Collection with AI-Powered Tools  

AI tools handle large datasets effortlessly, providing real-time insights that surpass traditional methods. Based on my experience,  embedding AI into the data collection phase of evaluation enhances efficiency, accuracy, and the ability to handle large volumes of data, ultimately leading to real-time monitoring systems for more informed and comprehensive evaluations Catching the Wave: Harnessing Data Science to Support Evaluation’s Capacity for Making a Tranformational Contribution to Sustainable Development.

Diverse Data Sources and Balanced Analysis 

Embedding AI within data analysis helps to efficiently gather and organize data, streamlining the entire collection process, and fostering precision. As well as a more comprehensive understanding of the assessment procedure, ultimately bolstering decision-making abilities and enhancing outcomes. Evaluation and Artificial Intelligence

Reporting and Visualization Enhanced by AI  

AI’s impact extends to reporting and visualization. Automated tools and dynamic techniques enhanced me to present findings effectively and compellingly. 

Lessons Learned

AI automates critical processes in the evaluation framework designing efficiency and precision. Here are some strategies I have found useful:

  1. Leveraging real-time data whenever feasible: Using AI tools for real-time data collection provides immediate insights and helps adjust strategies quickly.
  2. Automate routine analysis: Using AI tools handles repetitive data analysis tasks, which freed to focus on complex evaluation aspects, and machine learning algorithms enable evaluators to gain deeper insights and identify trends that may have otherwise been overlooked.
  3. Ensure data quality: Check for data accuracy and reliability, as AI’s effectiveness hinges on the quality of data processed.

Embracing AI in evaluation processes, human oversight remains essential. As evaluators, its important to be mindful of the ethical considerations by ensuring bias-free evaluations and safeguarding data privacy.  However, the collaboration between evaluators and AI systems is crucial in unlocking new understanding and efficiency, marking a new chapter in data-driven decision-making. 

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