It is based on the article ‘Dealing with algorithmic bias in news‘, which we recommend trainers read before adapting this outline for your own purposes.

Learning objective

Students will identify and analyse instances of algorithmic bias in news content. They will evaluate the impact of such biases on news quality and credibility, using real-world case studies.

• Student-facing objective: By the end of this lesson the student will be able to spot and assess algorithmic bias in news stories and understand how it affects the news we read and produce.
• Standards: Journalist will learn how to spot and deal with algorithmic bias in news

Learning activities

Warm-up

Display a short news headline generated by an algorithm. Ask students: “What do you notice? What do you wonder?” Give them a few minutes to think and discuss with a partner. Then, select a few students to share their observations and questions. Record these for all to see. Guide the conversation towards recognising potential biases in the headline, such as language choice or topic emphasis. This primes students for deeper exploration of algorithm bias in news.

Direct instruction

• Introduction to algorithmic bias: Explain the concept of algorithmic bias. Define key terms: selection bias, confirmation bias, and representation bias. Use a real example, such as a news recommendation engine that consistently suggests similar types of stories, to illustrate how bias can manifest. Discuss the impact of these biases on news quality and credibility.
• Sources of algorithmic bias: Describe the origins of algorithmic bias. Highlight three main sources: biased data, biased algorithms, and lack of transparency. Provide a case study, such as the Facebook Cambridge Analytica scandal, to demonstrate how biased data can lead to skewed news coverage. Encourage students to consider how transparency in algorithm design could mitigate these issues.
• Case studies and lessons learned: Present a case study where algorithm bias affected news coverage, such as the YouTube algorithm’s role in spreading misinformation. Discuss the lessons learned from this case. Ask students to identify signs of bias and suggest strategies for reducing its impact. Emphasise the importance of human oversight and editorial judgment in balancing algorithmic recommendations with journalistic integrity.

Guided practice

Think, Pair, Share: Guide students through a structured discussion to identify algorithmic bias in news content.

• Think: Present a news article generated by an algorithm. Ask students to individually identify potential biases, focusing on language, source selection, and representation.
• Pair: Have students pair up to discuss their findings. Encourage them to compare observations and refine their understanding of algorithmic bias.
• Share: Facilitate a class discussion where pairs share their insights. Record key points on the board, highlighting common biases and unique observations.
• Connect: Relate the discussion back to the case studies covered in direct instruction. Ask students to connect their findings with real-world examples.
• Reflect: Conclude with a reflection on how recognising algorithmic bias can improve news quality and credibility. Encourage students to consider the role of journalists in mitigating bias.

Independent practice

• Bias detection exercise: Provide students with a set of news articles generated by algorithms. Instruct them to identify and annotate instances of potential bias, focusing on language, source selection, and representation. Use a digital tool or printed copies for annotations.
• Analysis and reflection: Ask students to write a brief analysis of their findings, discussing the impact of identified biases on news quality and credibility. Encourage them to propose strategies for mitigating these biases.
• Peer review: Pair students to exchange their analyses. Have them provide constructive feedback on each other’s work, focusing on the clarity of bias identification and the feasibility of proposed strategies.

Assignment

Ask students:

1. What is one example of algorithmic bias you identified in today’s lesson?
2. How can journalists help mitigate algorithmic bias in news content?
3. What’s one question you still have from today’s lesson?

Suggested answers:

• Suggested answer to Question 1: An example is the YouTube algorithm promoting misinformation by favouring sensational content.
• Suggested answer to Question 2: Journalists can advocate for transparency in algorithm design and apply human oversight to ensure balanced news coverage.

Teacher resources

Differentiation guide

• Advanced learners: Encourage deeper analysis by having them explore additional case studies or research articles on algorithmic bias. Challenge them to propose innovative solutions for mitigating bias, considering ethical implications and technological advancements.
• Striving learners: Simplify concepts by using more relatable examples and providing additional scaffolding, such as graphic organisers or guided notes. Offer more structured support during activities, such as pairing them with peers who can assist in discussions and exercises.
• Recommended reading: This lesson plan is based on the article ‘Dealing with algorithmic bias in news‘, which we recommend trainers read before adapting this outline for your own purposes.

Notable definitions

Algorithmic bias: The systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.

Selection bias: A type of bias caused by selecting non-random data for analysis, which can lead to inaccurate conclusions.

Confirmation bias: The tendency to favour information that confirms existing beliefs or hypotheses, often leading to skewed data interpretation.

Required materials

• Computers: For accessing digital news articles and annotation tools.
• Printed copies: News articles for annotation exercises (optional).
• Digital annotation tools: Software or apps for marking up digital articles.
• Whiteboard/Markers: For recording observations and discussion points.
• Projector: To display news headlines and case studies.
• Notebooks/Pens: For students to take notes and write reflections.

Lesson summary

• Warm-up
• Direct instruction
• Guided practice
• Independent practice
• Assignment

The free teaching tools at the Khan Academy were used in the production of this lesson plan.

Related article

Dealing with algorithmic bias in news

The post Lesson: Algorithmic bias first appeared on Media Helping Media.

Learning objective

Students will evaluate news articles to assess the fairness of reporting by examining multiple perspectives and verifying facts. They will also critique the language and tone used to ensure accurate representation of the facts.

• Student-facing objective: By the end of this lesson the student will be able to check if all sides of a story are covered and make sure the language used is fair and accurate.
• Standards: After this lesson a student will understand the importance of exploring all sides of an issue and reporting their findings accurately.

Learning activities

Warm-up

• Begin with a brief discussion on the concept of fairness. Ask students to think about a time when they felt a story or report was unfair or one-sided. Encourage them to share examples without naming specific media outlets.
• Next, present a simple, hypothetical news headline on the board. Ask students to brainstorm different perspectives that might be included in a fair report on the topic.
• Conclude by asking students to consider why it’s important to include multiple perspectives in journalism. This primes them for the lesson’s focus on fairness and balanced reporting.

Direct instruction

• Introduce main concepts: Explain the importance of fairness in journalism. Discuss how journalists must explore all sides of an issue and report findings accurately. Highlight the responsibility to avoid personal bias and ensure language and tone do not misrepresent facts. Use real-world examples, such as a recent news story, to illustrate these points.
• Analyse a news article: Provide students with a news article. Guide them through identifying potential bias and assessing the fairness of the reporting. Ask them to consider if all relevant perspectives are included and if the language used is neutral. Use questions like: “What perspectives are missing?” and “How does the language influence the reader’s perception?”
• Discuss right of reply and editorial independence: Explain the concept of the right of reply and its importance in fair reporting. Discuss editorial independence and the need to maintain control over content. Use a hypothetical scenario where a journalist must decide whether to include a response from a controversial figure. Encourage students to think critically about the ethical implications and the balance between public interest and fairness.

Guided practice

Think, Pair, Share: Guide students through a structured activity to practice identifying bias and assessing fairness in journalism.

• Think: Provide students with a short news article. Ask them to individually read and note any potential bias, missing perspectives, or language that may misrepresent facts.
• Pair: Have students pair up to discuss their findings. Encourage them to compare notes and identify any additional biases or perspectives they may have missed individually.
• Share: Facilitate a class discussion where pairs share their insights. Encourage students to articulate how they identified bias and assessed fairness. Highlight diverse viewpoints and ensure all students understand the importance of multiple perspectives.
• Reflect: Ask students to reflect on how their understanding of fairness in journalism has evolved through this activity. Encourage them to consider how they might apply these skills in evaluating future news articles.
• Feedback: Provide feedback on their analysis, focusing on their ability to identify bias and assess fairness. Reinforce the importance of these skills in responsible journalism.

Independent practice

• Article analysis: Assign students a news article to read independently. Instruct them to identify any unfairness, missing perspectives, or language that may misrepresent facts. Provide a worksheet with guiding questions to support their analysis.
• Reflection: Ask students to write a brief reflection on how the article could be improved for fairness. Encourage them to suggest additional perspectives or changes in language that would enhance the article’s balance.
• Peer review: Pair students to exchange their analyses and reflections. Have them provide constructive feedback to each other, focusing on the identification of bias and suggestions for improvement.

Circulate to observe and support students as needed.

Assignment

Ask students to answer these questions:

1. How can a journalist ensure they are being fair when reporting a story?
2. Why is it important to include multiple perspectives in a news article?
3. What’s one question you still have from today’s lesson?

Here are some suggested answers:

• Suggested answer to Question 1: A journalist can ensure fairness by exploring all sides of an issue, verifying facts, and using neutral language.
• Suggested answer to Question 2: Including multiple perspectives ensures a balanced view and helps prevent bias, providing a more accurate representation of the facts.

Teacher resources

Differentiation guide

• Advanced learners: Encourage deeper analysis by having students compare multiple articles on the same topic from different sources. Ask them to evaluate the consistency of perspectives and language across these articles. Challenge them to identify subtle biases and discuss the potential impact on public perception.
• Striving learners: Simplify the task by providing a checklist of common biases and perspectives to look for in an article. Pair them with peers for collaborative analysis, allowing them to learn from others’ insights. Offer sentence starters to help them articulate their thoughts during discussions.
• Background reading: We recommend trainers read the article Fairness in journalism which is published on Media Helping Media before adapting this lesson plan for their own purposes.

Notable definitions

Fairness: Fairness in journalism is the concept of reporting news without bias or prejudice. It involves presenting all sides of an issue and reporting the facts accurately. Journalists should be objective and impartial, and they should not let their personal feelings or beliefs influence their reporting.

Bias: A tendency to present information in a way that reflects a particular perspective or preference, potentially leading to unfair or unbalanced reporting.

Right of reply: The opportunity given to individuals or groups to respond to allegations or criticisms made against them in a news report, ensuring fairness and balance.

Editorial independence: The freedom of journalists to report news without undue influence from external parties, allowing them to maintain objectivity and integrity in their reporting.

Required materials

• Copies of a news article for analysis
• Whiteboard and markers
• Worksheet with guiding questions for article analysis
• Access to computers or tablets (optional, for online article access)
• Reflection and peer review templates

Lesson summary

• Warm-up
• Direct instruction
• Guided practice
• Independent practice
• Assignment

The free teaching tools at the Khan Academy were used as a basis for converting the original article into a lesson plan.

Related article

Fairness in journalism

The post Lesson: Fairness in Journalism first appeared on Media Helping Media.

It’s based on the article Impartiality in journalism, which we suggest you read before adapting the lesson outline for your own purposes.

Learning objective

Students will evaluate news articles to identify instances of bias and impartiality. They will also create a short news piece that reflects a range of perspectives without personal bias.

Student-facing objective: By the end of this lesson the student will be able to spot bias in news articles and write a news piece that fairly includes different viewpoints.

Standards: The student will learn what it means to be impartial and how to produce news articles that are not prejudiced against any particular person, group, or point of view.

Learning activities

Warm-up

Notice and Wonder: Display a headline from a recent news article. Ask students, “What do you notice? What do you wonder?” Give them a few minutes to think and discuss with a partner. Then, have several students share their observations and questions. Record these for all to see. Guide the conversation towards noticing potential bias or lack of diverse perspectives in the headline. This primes students for evaluating bias and impartiality in news articles.

Direct instruction

• Conceptual understanding: Begin with a brief lecture on the definition of impartiality in journalism. Explain the main concepts such as bias, diversity of opinion, and the importance of reflecting multiple perspectives. Use real-world examples of news articles that demonstrate both impartial and biased reporting. Discuss how these examples either meet or fail to meet journalistic standards of impartiality.
• Procedural skills and fluency: Introduce a step-by-step process for evaluating news articles for bias. Outline the following steps:
  • Identify the main topic and stakeholders involved.
  • Analyse the language used for any emotive or loaded terms.
  • Check for the presence of multiple perspectives and whether they are fairly represented.
  • Evaluate the sources cited and their potential biases.

Provide a sample news article and guide students through this process, prompting them to apply each step.

• Application: Assign students to work in small groups to create a short news piece on a current event. Instruct them to:
  • Research the event using multiple sources.
  • Ensure their piece includes a range of perspectives.
  • Avoid personal bias in their writing.

After completion, have each group present their news piece to the class, explaining how they ensured impartiality. Facilitate a class discussion to critique and provide feedback on each group’s work.

Guided practice

Think, Pair, Share: Guide students through a structured activity to practice identifying bias and ensuring impartiality in news articles.

• Think: Provide each student with a short news article. Ask them to individually read and identify any potential bias or lack of diverse perspectives. Encourage them to note specific language or omissions that suggest bias.
• Pair: Have students pair up to discuss their findings. Instruct them to compare notes and discuss any differences in their observations. Encourage them to consider why they might have identified different biases.
• Share: Facilitate a class discussion where pairs share their findings. Encourage students to explain their reasoning and provide evidence from the text. Record key points on the board to highlight common biases and strategies for achieving impartiality.
• Reflect: Ask students to reflect on how their understanding of bias and impartiality has evolved through the activity. Encourage them to consider how they can apply these insights to their own news writing.
• Feedback: Provide feedback on their analysis, focusing on their ability to identify bias and suggest improvements for achieving impartiality. Reinforce the importance of diverse perspectives in journalism.

Independent practice

• Assign students an exercise to evaluate a news article for bias and impartiality. Provide a list of articles from various sources.
• Instruct students to apply the steps learned in class to identify bias, analyse language, and assess the representation of perspectives.
• Direct students to write a brief analysis of their findings, focusing on how the article meets or fails to meet standards of impartiality.
• Encourage students to reflect on their analysis and consider how they would revise the article to improve impartiality.
• Circulate to observe and support students as needed.

Assignment

Ask students to answer these questions

• How can you identify bias in a news article?
• Why is it important to include multiple perspectives in journalism?
• What’s one question you still have from today’s lesson?

Here are some suggested answers:

• Suggested answer to Question 1: Look for emotive language, lack of diverse perspectives, and biased sources.
• Suggested answer to Question 2: It ensures a comprehensive and fair representation of the issue, allowing the audience to form their own opinions.

Teacher resources

Differentiation guide

• Advanced learners: Encourage deeper analysis by having them compare multiple articles on the same topic from different sources. Ask them to evaluate the subtle biases and editorial choices that influence the presentation of facts. Challenge them to rewrite a biased article to achieve greater impartiality.
• Striving learners: Simplify the task by providing articles with clear examples of bias. Use guided questions to help them identify bias and diverse perspectives. Pair them with peers for collaborative analysis and support. Offer sentence starters to assist in writing their news piece.
• General strategies: Use visual aids to illustrate key concepts. Provide checklists for evaluating bias and ensuring impartiality. Allow students to work in pairs or small groups to foster peer learning and support.
• Background reading: We suggest you read Impartiality in journalism on Media Helping Media before planning this lesson.

Notable definitions

Impartiality: The practice of not being prejudiced against any particular person, group, or point of view in journalism. It involves delivering news that reflects a wide range of opinions and perspectives fairly and accurately.

Bias: A tendency to present information in a way that is partial or prejudiced, often favouring one side over another. In journalism, bias can manifest through language, omission of perspectives, or selective reporting.

Diversity of opinion: The inclusion of a variety of viewpoints and perspectives in news reporting. It ensures that no relevant perspective is ignored and that the audience receives a comprehensive understanding of the issue.

Required materials

• Recent news articles for analysis
• Projector or screen for displaying headlines
• Printed copies of sample news articles
• Writing materials (paper, pens)
• Access to computers or tablets for research
• Whiteboard and markers for recording observations and feedback

Lesson summary

• Warm-up
• Direct instruction
• Guided practice
• Independent practice
• Assignment

The free teaching tools at the Khan Academy were used as a basis for converting the original article into a lesson plan.

Related article

Impartiality in journalism

The post Lesson: Impartiality first appeared on Media Helping Media.

Journalists need to be trained in how to recognise and deal with algorithmic bias in order to counter the speed and scale at which biased content spreads.

That’s the view of Gemini, the artificial intelligence (AI) conversation tool, which says that the need for such training is “more urgent than ever”.

According to AI:

“the rapid advancement of artificial intelligence and machine learning has led to a significant increase in the use of algorithms in news production…

… algorithms can introduce bias into news content, which can have serious consequences for both individuals and society.”

Media Helping Media (MHM) asked Gemini how journalists can recognise and deal with algorithmic bias.

We also asked it to design a one-day training course to equip journalists with the tools they need to tackle the issue.

The following is the text of that conversation, which took place in September 2024. During the conversation Gemini states that while the challenge of bias in news production is not entirely new, the rise of AI has made it more urgent and complex.

AI warns that:

“journalists and news organisations must adapt to this new reality by developing strategies to mitigate algorithm bias and ensure that the news they produce is fair, accurate, and representative”.

We then asked AI the following questions. The answers were immediate. Gemini’s responses are unedited other than to change them from American English to English English  (MHM’s preferred house style).

MHM: What is algorithmic bias in news production?

Gemini AI: Algorithmic bias in news production refers to the unintentional or intentional bias introduced into news content by the algorithms used to curate, personalise, or generate news. This bias can manifest in various ways, including:

• Selection bias: Algorithms may prioritise certain stories or topics over others, leading to a skewed representation of the news.
• Confirmation bias: Algorithms may reinforce existing beliefs or biases by recommending content that aligns with a user’s preferences.
• Representation bias: Algorithms may underrepresent certain groups or perspectives, leading to a lack of diversity in the news.
• Amplification bias: Algorithms may amplify certain narratives or misinformation, contributing to the spread of false or misleading information.

Factors contributing to algorithmic bias:

• Biased data: If the data used to train algorithms is biased, the algorithms will likely produce biased outputs.
• Biased algorithms: The design of algorithms themselves can introduce bias, such as by prioritising certain features or metrics over others.
• Lack of transparency: The lack of transparency in how algorithms work can make it difficult to identify and address bias.

Addressing algorithmic bias in news production requires a multi-faceted approach, including:

• Improving data quality: Ensuring that the data used to train algorithms is diverse, representative, and free from bias.
• Designing unbiased algorithms: Developing algorithms that are transparent, accountable, and resistant to bias.
• Promoting transparency: Increasing transparency in how algorithms are used and the decisions they make.
• Human oversight: Ensuring that human editors and journalists play a role in reviewing and curating news content.

By understanding and addressing algorithmic bias, news organisations can help to ensure that the news they produce is fair, accurate, and representative.

MHM: How can journalists avoid algorithmic bias if they are not in control of the algorithms?

Gemini AI: While journalists may not have direct control over the algorithms used in news production, they can take several steps to mitigate algorithmic bias:

1. Be aware of algorithmic bias: Journalists should understand the concept of algorithmic bias and how it can manifest in news content. This knowledge can help them identify potential biases and take steps to address them.
2. Critically evaluate news sources: Journalists should be sceptical of news stories generated or curated by algorithms. They should carefully evaluate the sources of information and verify the accuracy of the content.
3. Seek diverse sources: Journalists should strive to gather information from a variety of sources, including those that may not be prominently featured in algorithm-driven news feeds. This can help to ensure that the news they report is balanced and representative.
4. Use human judgement: Journalists should rely on their own judgement to assess the quality and relevance of news stories. They should be willing to override algorithmic recommendations if they believe that they are biased or misleading.
5. Advocate for transparency: Journalists can advocate for greater transparency in how algorithms are used in news production. This can help to identify and address biases and ensure that the news is produced in a fair and ethical manner.
6. Collaborate with data scientists: Journalists can collaborate with data scientists to understand how algorithms work and identify potential biases. This can help them to develop strategies for mitigating algorithm bias.

By taking these steps, journalists can help to ensure that the news they report is accurate, balanced, and free from algorithmic bias.

MHM: So mitigating algorithmic bias is a new task brought about by the advance of AI but should now become a crucial part of a news producer’s job?

Gemini AI: That’s a great observation. While the term ‘algorithmic bias’ may be relatively new, the underlying issue of bias in news production has existed for centuries. However, the advent of AI and machine learning has amplified the potential for bias to be introduced into news content at a larger scale and at a faster pace.

Here’s a breakdown of how traditional news production compared to AI-driven news:

Traditional news production:

• Human curation: News editors and journalists manually select and curate stories based on their judgement and expertise.
• Limited reach: News distribution was primarily through print and broadcast media, limiting the audience and potential for bias.

AI-driven news production:

• Algorithmic curation: Algorithms use data to recommend news stories to users, often based on their past behaviour and preferences.
• Vast reach: News can be disseminated instantly to a global audience through digital platforms, increasing the potential impact of bias.

Key differences:

• Scale: AI can process and analyse vast amounts of data, increasing the potential for bias to be introduced at a larger scale.
• Speed: AI can disseminate news at a much faster pace than traditional methods, making it difficult to identify and correct biases before they have a significant impact.
• Personalisation: AI can personalise news content to individual users, potentially reinforcing existing biases and limiting exposure to diverse perspectives.

Therefore, while the challenge of bias in news production is not entirely new, the rise of AI has made it more urgent and complex. Journalists and news organisations must adapt to this new reality by developing strategies to mitigate algorithmic bias and ensure that the news they produce is fair, accurate, and representative.

MHM: So would you say that dealing with algorithmic bias should now be part of every journalist’s basic training?

Gemini AI: Absolutely. Dealing with algorithmic bias should be a fundamental part of every journalist’s training in the digital age. Here’s why:

1. Understanding the problem: Journalists need to understand how algorithms work, how they can introduce bias, and the potential consequences of this bias.
2. Identifying and mitigating bias: Journalists should be able to identify signs of algorithmic bias in news content and take steps to mitigate its effects.
3. Evaluating sources: Journalists should be able to critically evaluate the sources of information they rely on, especially those that are generated or curated by algorithms.
4. Promoting diversity and inclusion: Journalists should strive to promote diversity and inclusion in the news they report, by seeking out a variety of sources and avoiding algorithmic biases that may underrepresent certain groups or perspectives.
5. Advocating for transparency: Journalists can advocate for greater transparency in how algorithms are used in news production, which can help to identify and address biases.

By incorporating training on algorithmic bias into journalism programmes, we can equip journalists with the skills and knowledge they need to produce fair, accurate, and representative news in the digital age.

MHM: Would you say that the need for journalists to be trained in dealing with algorithmic bias is urgent?

Gemini AI: Absolutely. The need for journalists to be trained in dealing with algorithmic bias is more urgent than ever. The rapid advancement of artificial intelligence and machine learning has led to a significant increase in the use of algorithms in news production. These algorithms can introduce bias into news content, which can have serious consequences for both individuals and society.

Here are some reasons why this training is urgent:

• Increasing reliance on algorithms: News organisations are increasingly relying on algorithms to curate, personalise, and generate news content. This means that the potential for algorithmic bias to impact news coverage is growing.
• The potential for harm: Algorithmic bias can lead to the spread of misinformation, the amplification of harmful stereotypes, and the underrepresentation of certain groups or perspectives. This can have serious consequences for individuals and society.
• The need for accountability: Journalists have a responsibility to ensure that the news they report is accurate, fair, and unbiased. This includes understanding and addressing the potential for algorithmic bias.

By training journalists in how to identify and mitigate algorithmic bias, we can help to ensure that the news we consume is reliable and trustworthy.

MHM’s conclusion

The fact that AI is warning that journalists need to be trained in recognising and dealing with algorithmic bias suggests that human judgement, journalistic ethics and editorial values are crucial in a world where artificial intelligence and machine learning is crafting so much content.

As Gemini says in the interview above, “the underlying issue of bias in news production has existed for centuries. However, the advent of AI and machine learning has amplified the potential for bias to be introduced into news content at a larger scale and at a faster pace.”

Questions

1. What is algorithmic bias in news production, and how can it manifest in news content?
2. Identify and describe two factors that contribute to algorithmic bias in news production.
3. Explain why the rise of AI has made the challenge of bias in news production more urgent and complex.
4. What are some steps journalists can take to mitigate algorithmic bias, even if they do not control the algorithms?
5. Discuss the role of human oversight in addressing algorithmic bias in news production.
6. How does algorithmic curation differ from traditional human curation in news production?
7. Why is it important for journalists to seek diverse sources when reporting news?
8. Evaluate the potential consequences of algorithmic bias on society and individuals.
9. How can journalists advocate for greater transparency in the use of algorithms in news production?
10. In what ways can collaboration between journalists and data scientists help address algorithm bias?

Answers

1. Algorithmic bias in news production refers to the unintentional or intentional bias introduced into news content by algorithms. It can manifest as selection bias, confirmation bias, representation bias, or amplification bias.
2. Two factors contributing to algorithmic bias are biased data, where the data used to train algorithms is biased, and biased algorithms, where the design of algorithms prioritises certain features or metrics over others.
3. The rise of AI has made bias in news production more urgent and complex due to the increased scale and speed at which AI can process and disseminate news, as well as the potential for personalised content to reinforce existing biases.
4. Journalists can mitigate algorithmic bias by being aware of it, critically evaluating news sources, seeking diverse sources, using human judgment, advocating for transparency, and collaborating with data scientists.
5. Human oversight involves journalists and editors reviewing and curating news content to ensure it is fair, accurate, and representative, thus helping to identify and correct algorithmic biases.
6. Algorithmic curation uses data to recommend news stories based on user behaviour and preferences, while traditional human curation relies on the judgment and expertise of news editors and journalists.
7. Seeking diverse sources is important to ensure balanced and representative news coverage, avoiding the reinforcement of biases that may underrepresent certain groups or perspectives.
8. Algorithmic bias can lead to the spread of misinformation, amplification of harmful stereotypes, and underrepresentation of certain groups, which can have serious consequences for individuals and society.
9. Journalists can advocate for transparency by pushing for clear explanations of how algorithms are used in news production and the criteria they use for decision-making.
10. Collaboration with data scientists helps journalists understand how algorithms work and identify potential biases, allowing them to develop strategies to mitigate algorithmic bias.

Lesson plan for trainers

If you are a trainer of journalists we have a free lesson plan: ‘Algorithmic bias’ which you are welcome to download and adapt for your own purposes.

The post Dealing with algorithmic bias in news first appeared on Media Helping Media.

During elections politicians will often accuse media organisations and their journalists of bias. We look at the challenges of producing fair election coverage.

This article was written after a training session in Belgrade, Serbia, about editorial ethics during election coverage. 

Types of election-related bias

• Deliberate bias
  • Suppression of important facts.
  • Distortion through selective emphasis.
  • Unequal airtime for candidates (e.g., lengthy coverage for incumbents, brief summaries for challengers).
• Subtle bias
  • Manipulative camera angles to inflate or diminish rally sizes.
  • Selective filming of candidates’ “best” or “worst” moments.
  • Over-reporting on incumbents simply because they are more active, without providing equal coverage to challengers.

“Horse race” coverage

• • “Horse race” is a style of political journalism that focuses on who is winning or losing in an election, rather than on the candidates’ policy positions or the substantive issues at stake.
  • It involves a heavy reliance on public opinion polls, tracking which candidate is “ahead” or “behind.”
  • There will be detailed analysis of poll numbers, including shifts in support among different demographics.
  • It involves coverage of campaign tactics, fundraising, and strategic manoeuvres.
  • It will include analysis of campaign “momentum” and predictions of who is likely to win.
  • It involves framing elections as a sporting event, with a focus on winners and losers.
  • Language and metaphors from sports, such as “front-runner,” “underdog,” and “battleground” are often used.
  • Less attention given to the candidates’ stances on important issues.
  • Tends to be a lack of in-depth analysis of the potential impact of their policies.
  • It can lead to a shallow understanding of the candidates and their platforms.
  • It can divert attention from the substantive issues that voters should be considering.
  • It can contribute to a sense that elections are simply a game, rather than a serious process of democratic decision-making.
  • By focusing on who is winning, it can make voters feel that their vote does not matter.
  • In essence, “horse race” coverage prioritises the spectacle of the election over its substance.

Countering bias in election reporting

• Transparency
  • Clearly label opinion pieces and editorials.
  • Disclose potential conflicts of interest.
• Fair coverage
  • Ensure appropriate airtime and coverage for all viable candidates.
  • Present diverse perspectives on the main issues.
  • Presenting the facts of each candidate, and their policies, without added opinion.
• Fact-checking
  • Rigorous verification of claims made by candidates and their campaigns.
  • Prompt correction of errors.
• Focus on substantive issues
  • Prioritise policy discussions and voter concerns over campaign theatrics.
  • Presenting the facts about policies, and how they will effect citizens.
• Voter engagement
  • Incorporate voter perspectives into news coverage.
  • Highlight voter concerns and questions.
  • Pay close attention to public questions, as they can be a good indicator of what the public cares about.
• Internal checks and balances
  • Implement editorial review processes to identify and correct bias.
  • Consider appointing an ombudsman to address reader complaints.
• Avoiding manipulation
  • Be aware of attempts by candidates to bypass traditional media and control their message.
  • Maintain journalistic independence.
  • Do not allow candidates to manipulate the narrative.

The role of the journalist

• Journalists serve as a crucial link between candidates and voters.
• They must strive for fairness and accuracy, avoiding the temptation to become a “veil” that obscures the truth.
• They should be aware that they are a filter, and that their own biases can effect the news that is presented.

Addressing media bashing

Candidates, especially those struggling in polls, may resort to “media bashing.” Journalists should remain professional and focused on their duties.

“Media bashing” refers to the act of strongly and often publicly criticising or attacking the media. This can take various forms, including:

• Verbal attacks
  • This can involve harsh criticism of news outlets, journalists, or specific news reports.
  • It can also include accusations of bias, dishonesty, or incompetence.
• Public campaigns
  • These campaigns may aim to discredit or damage the reputation of media organisations.
  • They can involve spreading negative information or encouraging boycotts.
• Online attacks
  • “Media bashing” often occurs on social media, involving insults, harassment, and the spread of misinformation.
  • Politicians and public figures often engage in “media bashing” to deflect criticism or control the narrative.
  • It can be a tactic to undermine public trust in the media.

News values and priorities

• News outlets must be mindful of their news priorities. Focusing on internal campaign dynamics or personality-driven politics can detract from substantive issues.
• Studies show that voters often find debates more informative than traditional news coverage. Journalists should strive to bridge this gap by prioritising voter concerns.

 This article was written after a training session in Belgrade, Serbia, about editorial ethics during election coverage. 

The post Avoiding bias during election coverage first appeared on Media Helping Media.

Election coverage is one of the most critical responsibilities of the media. It shapes public discourse, informs voters, and plays a vital role in safeguarding democracy.

However, elections also present unique challenges to editorial independence. External pressures from political parties, advertisers, governments, and even audiences can threaten journalistic integrity. Maintaining independence is not only a professional obligation but essential for public trust.

This article is a combination of opinions shared with Media Helping Media by political journalists.

The importance of editorial independence

Editorial independence means that journalists and editors make decisions based on professional judgment, public interest, and factual reporting, free from influence by political, commercial, or personal interests.

During elections, this independence ensures that coverage is fair, and fact-driven, providing citizens with the information they need to make informed choices.

When the media compromises its independence, it risks becoming a tool for misinformation, polarisation, or partisan agendas—undermining the very democratic processes it seeks to support.

The following advice is compiled from contributions from political journalists in the Media Helping Media network.

1. Adopt and communicate clear editorial guidelines

Media houses should have well-defined editorial policies that emphasise fairness, accuracy, and independence. These guidelines should be publicly accessible to reinforce accountability and transparency.

2. Fact-check relentlessly

Elections are fertile ground for misinformation, propaganda, and manipulated narratives. Newsrooms must invest in robust fact-checking teams to verify claims made by candidates, political parties, and their supporters. Every piece of coverage should prioritise fact over sensationalism or speed.

3. Ensure diverse voices and perspectives

Fair and accurate reporting means giving space to a variety of political opinions, minority voices, and independent experts. Coverage should avoid amplifying only the most powerful or well-funded campaigns, ensuring marginalised groups and emerging political movements are heard.

4. Separate opinion from news

Clear demarcation between factual reporting and opinion pieces is crucial. Readers and viewers should be able to easily distinguish between analysis, editorial opinions, and hard news to avoid conflating bias with objectivity.

5. Resist commercial and political pressures

Advertisers or political interests may attempt to influence coverage, especially during high-stakes elections. Media organisations must be prepared to turn down advertising or sponsorship deals that compromise their editorial decisions. Internal barriers between business and editorial teams are essential.

6. Empower journalists with safety and support

Election coverage can expose reporters to harassment, online abuse, or physical danger. Media houses must prioritise journalists’ safety and mental well-being, ensuring that reporters can work without fear or intimidation, both of which can subtly erode independence.

7. Be transparent about mistakes and biases

If errors occur, acknowledge and correct them publicly. Transparency fosters trust and demonstrates a commitment to accountability. Likewise, openly addressing potential newsroom biases helps audiences understand the efforts made to maintain balance.

The role of media literacy

Finally, media organisations have a role in promoting media literacy during elections. By educating the public about how to identify credible information and question biased sources, the media empowers citizens to resist manipulation, thereby reinforcing the value of editorial independence.

Conclusion

Editorial independence is the backbone of credible election coverage. In a world of increasing political polarisation and misinformation, the media’s ability to report impartially is more important than ever. By adhering to professional standards, resisting external pressures, and focusing on the public good, journalists can ensure their election coverage serves democracy, not politics.

This article is a combination of opinions shared by political journalists in the Media Helping Media network.

The post Editorial independence during elections first appeared on Media Helping Media.