Last updated on May 18, 2024
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Cognitive bias
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Motivational bias
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Cultural bias
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Data bias
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Algorithmic bias
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Here’s what else to consider
Risk assessment is a vital process for any project or organization, as it helps to identify and prioritize potential threats and opportunities. However, risk assessment is not immune to human error, and sometimes bias can creep in and distort the results. Bias can be defined as a tendency to favor or disfavor a certain perspective, group, or outcome, regardless of the evidence or logic. Bias can affect risk assessment in various ways, such as influencing the sources of information, the methods of analysis, the interpretation of results, and the communication of findings. In this article, you will learn how to identify and avoid some common types of bias in risk assessment, and how to improve your risk management skills.
Key takeaways from this article
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Implement risk frameworks:
Create a system that grades risks and prescribes actions and timelines. It clarifies how to handle each level of risk, ensuring decisions are not left to subjective judgment alone.
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Acknowledge positive feedback bias:
Recognize that initial success doesn't always mean a good decision. Challenge successes and examine the decision process to prevent repeating mistakes based on positive outcomes alone.
This summary is powered by AI and these experts
- Akofa Dakwa, FRM, ACCA, ACIB Deputy Managing Director, Bank of…
1 Cognitive bias
Cognitive bias refers to the mental shortcuts or heuristics that people use to simplify complex decisions or judgments, often based on personal experience, beliefs, or emotions. Cognitive bias can affect risk assessment by causing people to overestimate or underestimate the likelihood or impact of certain risks, ignore or dismiss contradictory evidence, or seek confirmation of their existing assumptions. Some examples of cognitive bias are availability bias, anchoring bias, confirmation bias, and optimism bias. To avoid cognitive bias, you should try to use multiple sources of data, challenge your own and others' assumptions, seek diverse perspectives, and use structured tools and frameworks to guide your risk assessment.
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- Akofa Dakwa, FRM, ACCA, ACIB Deputy Managing Director, Bank of Africa Kenya || Board Member at Guinness Ghana Plc || Board Chairperson at Savvy Securities || ACCA exams award winner (top 0.2% worldwide)
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Risk management in organizations should culminate in the definition of various risk treatment actions intended to mitigate or monitor such risks. Meanwhile, there are various actors involved in risk management, from the Board of directors down to the front line staff in organizations; each with their own biases based on their individual experiences and backgrounds. What i have found useful is that, in order to minimise or eliminate the impact of biases in risk management, the organisation needs to implement a framework which clearly grades the residual risks identified. Each grade of residual risks will then have pre-defined actions and escalation matrixes and action timelines.
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Like Celebrate Support Love Insightful Funny 8 - Mohammed Diab Quality Manager @ Tecnimont | Talks about Quality Management | Aramco Approved QA Manager | LinkedIn Quality Management Top Voice | CMQ/OE, PMP, IRCA Principal Auditor QMS, NDT Level III | Author | Co-founder
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From my work in quality, I've seen that people sometimes rely too much on past experiences. For example, if something didn't go wrong before, they might think it won't happen again. Using clear methods can help us avoid this mistake.
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2 Motivational bias
Motivational bias refers to the influence of personal or organizational goals, incentives, or pressures on risk assessment. Motivational bias can affect risk assessment by causing people to manipulate or distort the data, analysis, or presentation of risks, to align with their desired outcomes, interests, or agendas. Some examples of motivational bias are self-serving bias, groupthink, framing effect, and moral hazard. To avoid motivational bias, you should try to establish clear and transparent criteria and standards for risk assessment, involve independent and objective parties, disclose any conflicts of interest or limitations, and foster a culture of openness and accountability.
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- Mohammed Diab Quality Manager @ Tecnimont | Talks about Quality Management | Aramco Approved QA Manager | LinkedIn Quality Management Top Voice | CMQ/OE, PMP, IRCA Principal Auditor QMS, NDT Level III | Author | Co-founder
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In my job, I've noticed that sometimes we might ignore risks to finish work faster. It's important to have a place where everyone can speak up without fear.
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3 Cultural bias
Cultural bias refers to the influence of social norms, values, or expectations on risk assessment. Cultural bias can affect risk assessment by causing people to adopt or reject certain risks, based on their cultural background, identity, or affiliation. Some examples of cultural bias are ethnocentrism, stereotyping, in-group bias, and halo effect. To avoid cultural bias, you should try to understand and respect the diversity of your stakeholders, avoid making generalizations or assumptions based on culture, seek feedback and input from different groups, and use culturally sensitive and inclusive language and communication.
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- Mohammed Diab Quality Manager @ Tecnimont | Talks about Quality Management | Aramco Approved QA Manager | LinkedIn Quality Management Top Voice | CMQ/OE, PMP, IRCA Principal Auditor QMS, NDT Level III | Author | Co-founder
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Working with different people, I've learned that what's okay in one culture might not be in another. It's good to learn from each other and understand different views.
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4 Data bias
Data bias refers to the influence of errors, gaps, or limitations in the data used for risk assessment. Data bias can affect risk assessment by causing people to base their decisions or judgments on incomplete, inaccurate, or outdated information. Some examples of data bias are sampling bias, measurement bias, survivorship bias, and recency bias. To avoid data bias, you should try to use reliable and relevant sources of data, verify and validate the data quality and accuracy, update and review the data regularly, and acknowledge and address any data limitations or uncertainties.
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- Mohammed Diab Quality Manager @ Tecnimont | Talks about Quality Management | Aramco Approved QA Manager | LinkedIn Quality Management Top Voice | CMQ/OE, PMP, IRCA Principal Auditor QMS, NDT Level III | Author | Co-founder
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In quality work, if we use bad data, we get bad results. Checking our data often and getting it from good sources can help avoid this.
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5 Algorithmic bias
Algorithmic bias refers to the influence of errors, gaps, or limitations in the algorithms or models used for risk assessment. Algorithmic bias can affect risk assessment by causing people to rely on flawed, biased, or unexplainable outputs or recommendations from the algorithms or models. Some examples of algorithmic bias are selection bias, encoding bias, feedback bias, and black box bias. To avoid algorithmic bias, you should try to use appropriate and validated algorithms or models, test and monitor the performance and outcomes of the algorithms or models, explain and justify the assumptions and logic behind the algorithms or models, and involve human oversight and intervention when needed.
Bias in risk assessment can have serious consequences for your project or organization, such as compromising the quality, credibility, or effectiveness of your risk management. By being aware of and avoiding the common types of bias, you can improve your risk assessment skills and deliver better results.
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- Mohammed Diab Quality Manager @ Tecnimont | Talks about Quality Management | Aramco Approved QA Manager | LinkedIn Quality Management Top Voice | CMQ/OE, PMP, IRCA Principal Auditor QMS, NDT Level III | Author | Co-founder
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With new computer tools, we must remember they work based on the data we give them. Checking their results and having people oversee them is important.
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6 Here’s what else to consider
This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?
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One very common bias is positive feedback; after making a wrong decision (actually increasing risk exposure) which leads to initial positive outcomes and therefore reinforces the belief that the decision was correct and increases its chance of being repeated.
Like Like Celebrate Support Love Insightful Funny 4 - Mohammed Diab Quality Manager @ Tecnimont | Talks about Quality Management | Aramco Approved QA Manager | LinkedIn Quality Management Top Voice | CMQ/OE, PMP, IRCA Principal Auditor QMS, NDT Level III | Author | Co-founder
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Risk work keeps changing. New things come up, and we must keep learning. In my job, I always try to learn more to do better. It's good to attend training and learn from others to avoid mistakes.
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