Responsible AI In Research And Why It Matters
Dec 18, 2024 |
9 Mins ReadArtificial Intelligence (AI) is changing how we live, work, and learn. However, as AI continues to evolve, it is important to ensure it is developed and used responsibly. In this blog, we’ll explore what responsible AI means, why it is essential, and how tools like ZAIA, Zendy's AI assistant for researchers, implement these principles in the academic sector.
What Is Responsible AI?
Responsible AI, also known as ethical AI refers to building and using AI tools guided by key principles:
- Fairness
- Reliability
- Safety
- Privacy and Security
- Inclusiveness
- Transparency
- Accountability
AI vs Responsible AI: Why Does Responsible AI Matter?
Keep in mind that AI is not a human being.
This means it lacks the ability to comprehend ethical standards or a sense of responsibility in the same way humans do. Therefore, ensuring these concepts are embedded in the development team before creating the tool is more important than building the tool itself.
In 2016, Microsoft launched a Twitter chatbot called "Tay", a chatbot designed to entertain 18- to 24-year-olds in the US to explore the conversational capabilities of AI. Within just 16 hours, the tool's responses turned toxic, racist, and offensive due to being fed harmful and inappropriate content by some Twitter users. This led to the immediate shutdown of the project, followed by an official apology from the development team.
In such cases, "Tay" lacked ethical guidelines to help it differentiate harmful content from appropriate content. For this reason, it is crucial to train AI tools on clear principles and ethical frameworks that enable them to produce more responsible outputs.
The development process should also include designing robust monitoring systems to continuously review and update the databases' training, ensuring they remain free of harmful content. Overall, the more responsible the custodian is, the better the child’s behaviour will be.
The Challenges And The Benefits of Responsible AI
Responsible AI is not a "nice-to-have" feature, it’s a foundational set of principles that every AI-based tool must implement. Here's why:
- Fairness: By addressing biases, responsible AI ensures every output is relevant and fair for all society’s values.
- Trust: Transparency in how AI works builds trust among users.
- Accountability: Developers and organisations adhere to high standards, continuously improving AI tools and holding themselves accountable for their outcomes. This ensures that competition centers on what benefits communities rather than simply what generates more revenue.
Implementing responsible AI comes with its share of challenges:
- Biased Data: AI systems often learn from historical data, which may carry biases. This can lead to skewed outcomes, like underrepresenting certain research areas or groups.
- Awareness Gaps: Not all developers and users understand the ethical implications of AI, making education and training critical.
- Time Constraints: AI tools are sometimes developed rapidly, bypassing essential ethical reviews, which increases the risk of errors.
Responsible AI and ZAIA
ZAIA, Zendy’s AI-powered assistant for researchers, is built with a responsible AI framework in mind. Our AI incorporates the six principles of responsible AI, fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability, to meet the needs of students, researchers, and professionals in academia. Here’s how ZAIA addresses these challenges:
- Fairness: ZAIA ensures balanced and unbiased recommendations, analysing academic resources from diverse disciplines and publishers.
- Reliability and Safety: ZAIA’s trained model is rigorously tested to provide accurate and dependable insights, minimising errors in output.
- Transparency: ZAIA’s functionality is clear and user-friendly, helping researchers understand and trust its outcomes.
- Accountability: Regular updates improve ZAIA’s features, addressing user feedback and adapting to evolving academic needs.
Conclusion
Responsible AI is the foundation for building ethical and fair systems that benefit everyone. ZAIA is Zendy’s commitment to this principle, encouraging users to explore research responsibly and effectively. Whether you’re a student, researcher, or professional, ZAIA provides a reliable and ethical tool to enhance your academic journey.
Discover ZAIA today. Together, let’s build a future where AI serves as a trusted partner in education and beyond.
ZAIA vs DeepSeek: Which AI Tool is Better For Researchers
AI tools have made handling complex tasks easier, but not all of them are created equal. While DeepSeek is widely known and accessible, it is a general-purpose AI tool, available to the public and easily understood by many. In contrast, ZAIA, an AI assistant for researchers developed by Zendy, takes a more specialised approach. Designed specifically for researchers and academics, ZAIA easily integrates into academic workflows with customised tools. Let’s take a closer look at how these two AI tools compare. What is DeepSeek DeepSeek AI is a Chinese conversational AI chat created by Liang Wenfeng, mostly used as a general-purpose AI tool that can assist with writing, brainstorming, coding, and more. It’s a Swiss Army knife, good for a lot of things but not customised to a specific need. This makes it a great option for users who need an all-in-one AI assistant rather than a highly specialised tool. What is ZAIA ZAIA, Zendy’s AI assistant, is built with researchers in mind. You can ask any research-related question, and ZAIA will analyse millions of academic papers to provide credible, reference-backed answers. In addition, It’s integrated into Zendy to give you access to over 40 million academic research papers and offers AI tools that make chatting, summarising, analysing, and extracting insights from research papers much simpler. ZAIA and DeepSeek Data & Knowledge Sources One of the key differences between ZAIA and DeepSeek is how they access and process information. ZAIA is designed by researchers for researchers, providing direct access to a vast collection of 40 million scholarly publications through Zendy’s massive digital library. This means researchers retrieve precise insights for their research, summarise it, and chat with it, all from credible, peer-reviewed sources. DeepSeek, on the other hand, is trained on a broad dataset covering various topics but lacks integration with academic databases like Zendy, PubMed or even SpringerLink, and cannot verify sources with citations. While it can generate general information, it lacks the ability to pull insights directly from academic literature, with a high possibility of predatory journals in the outputs. So, what does DeepSeek do better? As we mentioned above, it helps you in answering questions, brainstorming ideas, helping with writing, summarising information, and providing explanations on complex topics in a clear, understandable way. A Smarter, Lower Energy Consumption Choice Since AI became available to the public, concerns about energy consumption have begun to rise. For instance, DeepSeek-V3 required 2,788,000 GPU hours for training, equivalent to 836,400 kWh of energy. According to NDTV News, this consumes less energy compared to the current leading U.S. AI model, ChatGPT. According to RW Digital, the annual energy consumption for ChatGPT is expected to hit 226.8 GWh. To give you an idea of how much energy that is: It could fully charge 3.13 million electric vehicles, which is nearly 95% of all electric vehicles in the United States. It could power around 21,602 U.S. homes for a whole year. It would be enough to run the entire countries of Finland or Belgium for one day. On the other hand, ZAIA was developed to minimise energy usage even more, consuming as little as several kilowatts (kW) per server in data centres ZAIA’s focus on academic tasks means it requires fewer resources. This makes it an environmentally friendly choice for researchers who value sustainability. Who Should Use Which? If you’re looking for a general-purpose assistant to handle a variety of tasks, DeepSeek is a good option. But if you’re a student, researcher, or academic professional who works with research papers regularly, ZAIA is a better fit. It’s built to simplify the research process and provide you with the necessary AI tools and access to your academic needs, integrating AI in scientific research for enhanced productivity and quicker discoveries. Conclusion DeepSeek is a great all-around tool, but ZAIA’s focus on the academic field makes it the smarter choice for researchers. It’s affordable, consumes less energy and time, and is designed to make your academic journey smoother. That’s why, when it comes to AI tools, personalisation is the key. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
What is Synthesis in Research? Synthesis vs. Summerising vs. Analysis
Going through mountains of papers, studies, and data is a common task when working on a research project. Finding a logical approach to combine all of this information is the hard part. This process is called synthesis, a skill that can help make your research be clearer, more insightful, and more impactful. In simple terms, synthesis in research means combining ideas from different sources to create a new understanding. But here’s the thing: synthesis is often confused with summarising or analysing, which can lead to unclear or incomplete results. In this blog, we’ll break down what synthesis really means, how it’s different from other processes, and how you can do it well. Let’s get started. What is Synthesis in Research? It's about connecting ideas, data, and findings from multiple sources to generate a new perspective. It’s not just repeating what others have said—it’s about connecting the dots to see the bigger picture and how sources relate to your main idea For instance, suppose you are researching the effects of exercise on mental health. One study may indicate that yoga enhances mood, another suggests jogging lowers anxiety and a third that emphasises the advantages of team sports for social well-being. In order to conclude that different kinds of exercise have different positive effects on mental health, synthesis in research involves connecting these studies together. How is Synthesis in Research Different from Summarising and Analysing? A lot of researchers mix up synthesis with summarising or analysing. Here’s how they’re different: Synthesis vs. Summarising Summarising Summarising means condensing the key points of a source or multiple sources without adding new insights. Example: If three studies say that exercise improves mental health, a summary would list those findings without connecting them. Synthesis goes a step further. It combines ideas from multiple sources to create a new understanding. Example: Connecting findings about running, yoga, and team sports to show how different types of exercise benefit mental health in unique ways. Synthesis vs. Analysis Analysis involves breaking down a single source or idea to examine its parts, strengths, or weaknesses. It’s about looking closely at one piece of information. Example: Analysing a study on exercise and mental health might involve critiquing its methods or interpreting its results in detail. Synthesis does not just connect ideas but constructs a new argument or framework from them. It’s about seeing how different pieces of information relate to each other. Example: Synthesising multiple studies on exercise and mental health might reveal patterns, like how different types of exercise affect different aspects of mental health. Why Is Synthesis in Research Important? Because it helps you: See Connections: By combining insights, you can understand a topic more fully. Find Gaps: It helps you spot what’s missing in the existing research. Build Stronger Arguments: When you connect ideas from multiple sources, your conclusions become more convincing. Make Research Useful: In fields like policy or healthcare, synthesis helps turn research into practical solutions. Without synthesis, research can feel scattered or repetitive. It’s what ties everything together. How to Do Synthesis in Research: A Step-by-Step Guide Here’s a simple way to approach synthesis in research: 1. Start with a Clear Question Before diving into your sources, ask yourself: What am I trying to learn or answer? A clear question will help you stay focused and avoid getting overwhelmed. 2. Gather Your Sources Look for reliable, relevant studies, articles, and data. Use tools like Zendy to find the latest articles in one place and EndNote to keep everything organised. 3. Look for Patterns and Connections As you read, take notes on common themes, differences, or trends. For example, do multiple studies point to the same conclusion? Are there conflicting results? A table or chart can help you visualise these patterns. 4. Bring It All Together Combine the insights you’ve gathered into a cohesive narrative. Explain how the ideas relate to each other and to your research question. 5. Draw Your Conclusions Summarise what you’ve learned and explain why it matters. Be honest about any limitations and suggest areas for further study. How can you improve your synthesis skills? Read your sources multiple times. Make well-organised notes on each one. Find relevant ideas and evidence for them. Rearrange your notes based on concepts. Put ideas into an outline. Conclusion Synthesis in research is more than just combining information—it’s about creating new knowledge and understanding. By following the steps above, you can effectively bring together ideas, spot patterns, and produce work that’s clear, insightful, and useful. Whether you're a student, academic, or professional, synthesis can help you take your research to the next level. Struggling to make sense of your research? AI tools can assist with synthesis by identifying key themes and connections across multiple papers, check out our AI tools to simplify the process and get better results. Let’s make your research work for you! .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
JIF vs. SJR Ranking: What's The Difference?
Two of the most common journal rankings are the Journal Impact Factor (JIF) and SCImago Journal Rank (SJR). They both measure journal impact but in different ways. Knowing how they work can help you choose the right journal to publish in. If you're deciding where to publish your research or assessing the significance of journals in your field, understanding these metrics can guide your choices. While both JIF and SJR aim to measure journal impact, they approach it differently. Depending on your needs, you might prefer one over the other or consider both for a well-rounded view. What Is SJR? Developed by SCImago using Scopus's database, SJR ranking evaluates the scientific impact of journals on their citations. Unlike a simple citation count, SJR assigns a higher value to citations from prestigious journals, meaning that not all citations carry the same weight. Key Features of SJR Ranking: Citation Quality Is Important: A journal's SJR ranking is influenced more by citations from reputable publications. Three-Year Citation Window: Citations are taken into account by SJR ranking for a duration of three years. Field-Normalised: SJR ranking takes into consideration variations among disciplines, which facilitates the comparison of publications from various fields of study. Open Access Inclusion: By incorporating open-access journals, SJR ranking provides a more comprehensive understanding of journal impact. What Is JIF? The Journal Impact Factor (JIF) provides one of the most well-known indicators of a journal, run by Clarivate and based on Web of Science database. Unlike SJR ranking, JIF is calculated by a straightforward ratio: the number of citations a journal receives in a given year divided by the number of citable articles published in the previous two years. Key Features of JIF: Two-Year Citation Window – Focuses on recent citations. Straightforward Calculation – It is simple to understand because it uses a simple average. Exclusive to Web of Science – Only Web of Science-indexed journals are included. Heavily Used in Academic Promotion – JIF is often a deciding factor in funding applications and tenure evaluations. Which Ranking Should You Use? It depends on what you're looking for. If you need a broader view of journal impact that considers citation prestige, SJR ranking may be more useful. If you're in a field where the Journal Impact Factor is commonly used for assessments (e.g., life sciences, medicine), JIF might be the better choice. If you're working with open-access journals, SJR provides a more inclusive perspective. In Conclusion While each ranking system has advantages, none of them is a perfect measurement of journal ranking quality. To have a better view of a journal's influence, think about examining a variety of classifications rather than depending solely on SJR ranking or JIF ranking. Knowing these distinctions will help you make well-informed choices, whether choosing a journal for publication or assessing a research paper. Sources: Clarivate Analytics Impact Factor SCImago Journal Rank (SJR) .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }