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Omitted variable bias occurs when a statistical model fails to include one or more relevant variables. In other words, it means that you left out an important factor in your analysis.

Example: Omitted variable biasLet’s say you want to investigate the effect of education on people’s salaries. In order to correctly analyze this effect, you should also include ability in your model. Ability makes a student more successful than their peers in school, which may lead to a better job and a better salary after graduation.

If you don’t have a trustworthy measure of ability, you may have to exclude it from your model despite knowing that it’s an important variable.

In this case, excluding ability causes omitted variable bias. This may lead to an overestimation or under-estimation of the effect of your other variables.

As a result, the model mistakenly attributes the effect of the missing variable to the included variables. Exclusion of important variables can limit the validity of your study findings.

What is an omitted variable?

An omitted variable is a confounding variable related to both the supposed cause and the supposed effect of a study. In other words, it is related to both the independent and dependent variable.

Example: Omitted variableLet’s revisit the example of the effect of education on salaries.

Here, the independent variable is education. However, salary is also likely to be related to ability, which you previously decided to exclude. In turn, ability is also likely related to the level of education a person attains, as those with greater ability are likely to pursue higher education.

The omitted variable (ability) affects your analysis of both education (the independent variable) and earnings (the dependent variable).

While a variable can be omitted because you are not aware that it exists, it’s also possible to omit variables that you can’t measure, even though you are aware of their existence.

What is omitted variable bias?

Omitted variable bias occurs in linear regression analysis when one or more relevant independent variables are not included in your regression model.

A regression model describes the relationship between one or more independent variables (also called predictors, covariates, or explanatory variables) and a dependent variable (often called a response or target variable).

Because the omitted variable is hidden or unobserved, it’s not factored into your analysis, affecting your results.

This can bias your coefficients if the omitted variable is correlated with either:

The dependent variable

One or more other independent variables

Example: Biased coefficientsLet’s consider the simple linear regression formula for the effect of education on salaries:

Salary = β0 + β1 ∗ Educ + ε

Where:

Salary is the wage in dollars (dependent variable)

Educ is the years of education completed (independent variable)

β0 is the intercept, or the predicted value of Salary when Educ is 0

β1 is the regression coefficient, or how much we expect salaries to change as education increases.

ε is the error term, showing how much variation there is in our estimate of the regression coefficient.

As we saw, ability is the omitted variable in this model—it’s absent, but it shouldn’t be. Ability is correlated with both salary and education. Since it is not included in our regression model, we conclude that it’s “hiding” somewhere. But where?

Why is omitted variable bias a problem?

An omitted variable is a source of endogeneity. Endogeneity occurs when a variable in the error term is also correlated with an independent variable.

When this happens, the causal effect from the omitted variable becomes tangled up in the coefficient on the variable with which it is correlated. This, in turn, undermines our ability to infer causality and severely impacts our results.

Example: EndogeneityGoing back to our example, ability is in the error term due to endogeneity. It is correlated with the independent variable, as people with high ability also tend to achieve a higher level of education.

Since ability is not in the regression model, our estimate of β1 will absorb some of the effect of ability.

The estimate is now biased, so we can no longer make a causal claim about education.

Omitting a variable might lead to an overestimation (upward bias) or underestimation (downward bias) of the coefficient of your independent variable(s). Since the coefficient becomes unreliable, the regression model also becomes unreliable.

How to deal with omitted variable bias

Regression models cannot always perfectly predict the value of the dependent variable. Thus, every regression model has one or more omitted variables. While it can’t be avoided altogether, there are steps you can take to mitigate omitted variable bias.

If the required data are not available, like in the case of ability, you can use control variables. Taking the example of salaries, controls are variables that in theory affect salary, such as years of work experience.

If you don’t have the data, use proxies for the omitted variables. These are variables that are similar enough to the omitted variable to give you an idea about its value, but that you are able to measure. For example, you might use an IQ test as a proxy for an individual’s ability.

If you are not able to resolve the research bias, try to make a prediction about which direction your estimates are biased. This is called “signing” the bias. You can sign it as either positive or negative, and this helps you estimate the omitted variable bias.

Estimating omitted variable bias

The table below summarizes the direction of the omitted variable bias. The sign of the bias is based on the sign of the relationships between the omitted variables and the variables in the model.

Let’s assume:

B is another independent variable, the omitted variable.

A and B are positively correlated A and B are negatively correlated

B has a positive effect on Y Positive bias Negative bias

B has a negative effect on Y Negative bias Positive bias

Note that with positive bias, we tend to overestimate, while with negative bias, we tend to underestimate.

Example: Estimating omitted variable biasWe can now make a logical conjecture about how ability affects education, as well as how ability affects salary.

As a reminder, our regression as it stands now is:

Salary = β0 + β1 ∗ Educ + ε

While it should be:

Salary = β0 + β1∗ Educ + β2 ∗ Abil +ε

At the same time, the higher the ability, the higher the education level completed. Therefore, we can conclude that:

Salary and education are positively correlated

Education and ability are positively correlated

What does this imply for our regression analysis? We know that education is likely to lead to higher salary. At the same time, someone with a higher level of education likely has a higher level of ability.

When omitting the ability variable, we see that the education variable may actually also be accounting for the effects of ability, and not just education.

Thus, β1 suffers from bias. More specifically, it suffers from upward bias because both ability and education have a positive effect on salary. Leaving out ability lets the coefficient of education pick up parts of the positive effects of ability.

Since ability is likely to be positively correlated with both salary and education, we can conclude that the effect of education on salary is overestimated in our analysis.

Other types of research bias Frequently asked questions Sources in this article

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What Is Bias And Variance In Machine Learning?

Overview of  Bias-Variance

As we dredge into the fascinating world of machine learning, we come across two fundamental concepts that refer to the different sources of errors in model predictions: Bias and Variance.

Bias is the difference between the expected or average predictions of a model and the actual value. Conversely, variance refers to how much the model’s prediction varies for different training sets. Bias and variance hold immense significance in determining the accuracy and performance of a machine-learning model. In this article, we will study what Variance and Bias mean in the context of machine learning, how they affect the model’s performance, and why it is essential to understand their trade-offs.

Key Takeaways of Bias-Variance

Bias measures the error caused by a model’s tendency to make incorrect data assumptions consistently.

Variance measures the error caused by a model’s tendency to overfit to the specific training data.

Techniques such as regularization and cross-validation can reduce variance and bias, respectively.

Reducing bias and variance improves the generalization performance of a machine learning model and enhances its interpretability.

What is Bias in Machine Learning

Hadoop, Data Science, Statistics & others

For example, suppose we have a regression problem where we are trying to predict the price of a house based on its features, such as the number of bedrooms, bathrooms, and square footage. Suppose we use a linear regression model that is too simple and only considers the number of bedrooms as a feature. In that case, the model may consistently underestimate or overestimate the actual price, leading to a high bias.

It’s important to note that some bias is inevitable in machine learning models. However, minimizing bias as much as possible can lead to more accurate and fair predictions. Techniques such as regularization can also be used to reduce bias and improve the model’s generalization performance.

There are two types of bias as follows.

Low Bias: It makes a few assumptions about the specified targeted function.

High Bias: It is used to make more assumptions compared to low bias but is incapable of handling new data.

What is Variance in Machine Learning

In machine learning, variance measures the sensitivity of the model’s performance to the specific data set used for training. A model with high variance is said to be overfitting the data, meaning it is too complex and has learned to memorize the training data instead of generalizing to new data. This can happen when the model is too flexible or has too many parameters relative to the amount of training data.

On the other hand, a low variance model is too simple and has not learned enough from the training data. This means it may underfit the data and not capture all essential patterns.

For example, suppose we have a regression problem where we are trying to predict the price of a house based on its features, such as the number of bedrooms, bathrooms, and square footage. If we use a polynomial regression model with a very high degree, the model may fit the training data very closely. Still, it may not generalize to new data, leading to high variance.

What effect does it have on the machine learning model?

Let’s consider the relationship between bias-variance for better understanding.

High Bias and High Variance: It gives the inconsistent result as well as it is not providing accurate results

High Bias and Low Variance: It provides a consistent result, but on average, it is low.

Low Bias and High Variance: This model provides an accurate result compared to the above two, but the average of this model is inconsistent.

Low Bias and Low Variance: This is the most consistent case because it provides a consistent result, and the average is too good.

Below the diagram, we can see a graphical view of the above four relationships.

Even though distinguishing predisposition and change in a model is very obvious, a model with a high difference will have a common preparation blunder and high approval mistake. Also, because of high predisposition, the model will have big preparation mistakes, and an approval blunder is equivalent to a preparing blunder. Based on the overview, it helps to reduce the real-time task if we have the following things as follows.

We need to add more input features while working.

We need to reduce the complexity when implementing the polynomial features.

It reduces the regularization of terms

It provides more training data.

Understanding Bias-Variance Trade-off

Finding the proper harmony between the inclination and fluctuation of the model is known as the Predisposition Difference compromise. Regardless, it is essentially a method for ensuring the model is neither overfitted nor under fitted.

What is the importance of Bias and Variance?

We know that machine learning algorithms use mathematical and statistical models with two types of error: reducible and irreducible. Unchangeable or irreducible error is because of normal changeability inside a framework.

The importance of bias and variance lies in finding a balance between them. A good machine learning model should have low bias and low variance, which means it accurately captures relevant patterns in the data without overfitting or underfitting. Achieving this balance requires careful tuning of hyperparameters, selecting appropriate features, and choosing a suitable algorithm.

Conclusion

Finally, by controlling bias-variance, we can improve the interpretability of a machine learning model, meaning we can gain insights into the underlying patterns in the data and how they relate to the output variable. The ability to interpret and explain a model’s predictions or decisions can be crucial in various real-world applications.

Recommended Articles

We hope that this EDUCBA information on “Bias-Variance” was beneficial to you. You can view EDUCBA’s recommended articles for more information.

Can I Use A Javascript Variable Before It Is Declared?

The behavior in which a variable appears to be used before it’s declared is known as hoisting.

For example, the following,

rank = 5; var rank;

The above works the same as the following −

var rank; rank = 2;

Hoisting in JavaScript, allows us to declare variables, functions, or classes that are moved to the top of the scope preceding the execution of the code regardless of whether their scope is local or global.

JavaScript allocates memory for all the variables and functions before their execution. Note that hoisting means, declaring not initializations.

By being variables and functions used before they are declared in the code, there is a chance to get unexpected errors. So, hoisting is not recommended use. Hoisting will work differently with var, let, and const. We can check the below in detail.

First, we will check examples of hoisting with var. Later we will check in detail about variable hoisting.

Example

x = 10; document.getElementById(“output”).innerHTML = x; var x;

Here, we can see that we have used the x variable before it is declared.

Now, we will check JavaScript hoists only declarations but not initializations. Let’s see another example

Example

x = 10; document.getElementById(“output”).innerHTML = x + ” , ” + y; var x; var y = 20;

Here, we can check hoisting means y needs to be initialized before it is used. But y is not initialized. So, this is not hoisted. That’s why the y value is undefined. We can write properly as mentioned in example 1.

Example

var x = 10; var y; document.getElementById(“output”).innerHTML = x + ” , ” + y; y = 20;

Here, the variable y is declared but not initialized before we display.

JavaScript only hoists declaration, not initialization.

Always note that, in the background, JavaScript first declares the variables and initializes value to them. In JS, undeclared variables do not exist until the assigned code is executed. So, undeclared variables are considered global variables when the assignment code is executed. Let’s look deeper into variable hoisting.

let and const hoisting

We all know let and, const are block scoped not function scoped. The let and const are introduced in ES6. We know ES6 will not allow undeclared values. If we try to use variables before the declaration, it will throw a reference error. Unlike var, variables are not initialized with the default value. Let’s see an example

Example

try{ document.getElementById(“output”).innerHTML = x; let x = “Hi”; } catch(err) { document.getElementById(“output”).innerHTML = err; }

Here, we can observe that we are trying to access x value before its declaration. Let’s see another example with let.

Example

let x; document.getElementById(“result1”).innerHTML = x; x = 5; document.getElementById(“result2”).innerHTML = x;

Now, let’s check with const

Hoisting with const

Like let, we can’t use variables before the declaration, and also we can not only declare variables but also need to initialize the value to them. Otherwise, it will throw a SyntaxError error. Let’s see an example

Example

const x; document.getElementById(“output”).innerHTML = x; x = 5; document.write(x);

We need to initialize the value while doing the declaration.

Example

try{ document.getElementById(“output”).innerHTML = x; const x = 5; }catch(err){ document.getElementById(“output”).innerHTML = err; }

Like, the above example is also not possible with const. It will throw a reference error.

Now, let’s see hoisting with functions.

Function hoisting

Function hoisting allows us to call a function before it is defined. Let’s see an example

Example

getName(“Devika”); function getName(name) { document.getElementById(“output”).innerHTML = (“Employee name is ” + name); }

If the function expressions assigned to the variables. An output will depend on variable scope. Like,

Example

try{ getName(“Devika”); var getName = function (name) { document.getElementById(“output”).innerHTML = (“Employee name is ” + name); } }catch(err){ document.getElementById(“output”).innerHTML = err; }

Example

try{ getName(“Devika”); let getName = function (name) { document.getElementById(“output”).innerHTML = (“Employee name is ” + name); } }catch(err){ document.getElementById(“output”).innerHTML = err; }

Example

try{ getName(“Devika”); const getName = function (name) { document.getElementById(“output”).innerHTML = (“Employee name is ” + name); } }catch(err){ document.getElementById(“output”).innerHTML = err; }

Here we can observe, that functions with variables will work differently. If we assign function expressions as var, we are getting TypeError, and with let and const, we are getting ReferenceError.

However, using functions before their declarations is our personal matter of preference.

Hope this tutorial will give knowledge on hoisting in js and hoisting works with variables and functions.

Variable Rate Demand Note (Vrdn)

Variable Rate Demand Note (VRDN)

A a long-term floating rate instrument that carries an interest rate that accrues periodically in line with the current money markets

Written by

CFI Team

Published June 29, 2023

Updated June 28, 2023

What is a Variable Rate Demand Note (VRDN)?

A variable rate demand note (VRDN) is a long-term floating rate instrument. It carries an interest rate that accrues periodically in line with the current money markets. From the outset of the loan, the assigned interest rate is equal to the sum of unique money market funds plus an extra margin.

A VRDN is a long-term municipal bond, which carries a coupon that adjusts at regular intervals – usually 7 to 35 days – leading to a short-term duration asset. The bonds tendered are then resold to the secondary market by a reseller agent or trustee.

Typically, a VRDN includes a one or seven-day put option that allows investors to put the asset back to an agent to match the notice duration. The long-term bond helps the municipality to borrow funds with long maturities while paying investors short-term rates. It is offered through money market funds, especially to small investors, because it is issued at minimum denominations of $100,000.

Summary

A variable rate demand note is a debt instrument bearing a floating interest rate that allows an investor to put the stock back to a financial intermediary.

A variable rate demand note is provided with liquidity funding from banks and other financial institutions with a high credit rating.

The debt instrument supports the demand note making the credit enhancement an attractive feature for investment.

Understanding Variable Rate Demand Notes

Highly-rated banks and other financial institutions provide VRDNs with liquidity or external credit enhancement support to help put back the asset to an intermediary at par with the notice. The enhancement support allows payment of interest and principal through a letter of credit (LOC).

The issuing agreement requires investors to present a one-day or seven-day notification to enhance the liquidity of the security by tendering to a financial intermediary. The clear majority of the outstanding VRDNs are held by either municipal money market funds, individual investors, or other investors, such as bond funds and corporations.

One feature of the VRDN is that the debt is payable on demand, courtesy of the embedded put option. Lenders or investors can, therefore, request the entire debt amount to be repaid at their discretion. Once the demand’s been made, the funds are fully repaid at a go.

How a VRND Works

The following are the two main elements of a variable rate demand note:

1. Letter of Credit (LOC)

A LOC offers liquidity enhancement and is provided by third-party financial institutions, such as banks. In case the debt instrument includes an irrevocable LOC, the investor’s primary source of credit enhancement is viewed as changed from a municipal issuer to the LOC provider. It happens because, if the issuer cannot pay per to the investor, the LOC issuer can step in as the liquidity provider of last resort.

2. Standby Purchase Agreement (SBPA)

A Standby Purchase Agreement (SBPA) is different from the irrevocable LOC, in that it serves as a conditional liquidity facility for a VRDN. An SBPA can normally be terminated under the following conditions:

Underlying obligor suffers bankruptcy

Underlying obligor fails to reach the set investment grade

Underlying tax-exempt bonds are rendered taxable

Underlying obligor defaults

In some circumstances, the municipal government is forced to pay for the principle and interest, as well as providing for the par put because a particular VRDN includes neither an LOC nor an SBPA.

Putting Back the Variable Rate Demand Note

After one or seven days of written notice, investors can put their funds into the tender agent. Even so, the marketing resellers take back the VRDN directly from the owner so that it can be put back to a financial intermediary. More often, the resellers try to trade the VRDN within the window period.

At one extreme, an agent never needs to take possession of the VRDN if it is successfully remarketed; at the other, an agent must take the debt instrument into his inventory to facilitate par payment to an investor. Additionally, if the VRDN is not resold, the tender agent then initiates par pay for the notes by drawing on the LOC or SBPA.

The majority of money-making investors seek to select a VRDN whose guarantors are well-capitalized financial institutions. It makes the credit enhancement feature of VRDN an attractive investment option, given that it supports the demand note. In addition to improving the security’s credit rating, the enhancement feature lessens default risks of the underlying securities.

An investor is guaranteed interest payment as long as the banks or financial institutions providing the LOC are solvent. In such regard, the VRDN’s interest rate tends to mimic the short maturity credit profile of the liquidity provider, rather than that of the municipal issuer. Reputable banks may use bond purchase agreements to enhance credit by reducing default risk.

A VRDN bears an interest rate that lowly correlates with bonds and stocks. It makes the debt instrument suitable for investment diversification. While the VRDN can either be taxable or tax-free, municipality governments generally issue variable-rate demands that are tax-exempted by the federal government.

Additional Resources

Difference Between Local And Global Variable

Key Differences between Local Variable and Global Variable

The local variable is declared inside a function, whereas the Global variable is declared outside the function.

Local variables are created when the function has started execution and is lost when the function terminates, on the other hand, a Global variable is created as execution starts and is lost when the program ends.

The local variable doesn’t provide data sharing, whereas the Global variable provides data sharing.

Local variables are stored on the stack, whereas the Global variable is stored in a fixed location decided by the compiler.

Parameters passing is required for local variables, whereas it is not necessary for a global variable

What is a Variable?

Variable is a name assign to a storage area that the program can manipulate. A variable type determines the size and layout of the variable’s memory.

It also determines the range of values which need to be stored inside that memory and nature of operations that can be applied to that variable.

Scope of Variables

The scope of the variable is simply lifetime of a variable. It is block of code under which a variable is applicable or alive. For example:

function foo(){ var x; }

There are three places where variables you can declare variable programming language:

Inside a function or a block: Local variables

Outside of all functions: Global variables

In the definition of function parameters: Formal parameters

Local Variable

Local Variable is defined as a type of variable declared within programming block or subroutines. It can only be used inside the subroutine or code block in which it is declared. The local variable exists until the block of the function is under execution. After that, it will be destroyed automatically.

Example of Local Variable

public int add(){ int a =4; int b=5; return a+b; }

Here, ‘a’ and ‘b’ are local variables

Global Variable

A Global Variable in the program is a variable defined outside the subroutine or function. It has a global scope means it holds its value throughout the lifetime of the program. Hence, it can be accessed throughout the program by any function defined within the program, unless it is shadowed.

Example:

int a =4; int b=5; public int add(){ return a+b; }

Here, ‘a’ and ‘b’ are global variables.

Local Variable vs Global Variables

Here, are some fundamental differences between Local and Global variables.

Parameter Local Global

Scope It is declared inside a function. It is declared outside the function.

Value If it is not initialized, a garbage value is stored If it is not initialized zero is stored as default.

Lifetime It is created when the function starts execution and lost when the functions terminate. It is created before the program’s global execution starts and lost when the program terminates.

Data sharing Data sharing is not possible as data of the local variable can be accessed by only one function. Data sharing is possible as multiple functions can access the same global variable.

Parameters Parameters passing is required for local variables to access the value in other function Parameters passing is not necessary for a global variable as it is visible throughout the program

Modification of variable value When the value of the local variable is modified in one function, the changes are not visible in another function. When the value of the global variable is modified in one function changes are visible in the rest of the program.

Accessed by Local variables can be accessed with the help of statements, inside a function in which they are declared. You can access global variables by any statement in the program.

Memory storage It is stored on the stack unless specified. It is stored on a fixed location decided by the compiler.

Advantages of using Global variables

You can access the global variable from all the functions or modules in a program

You only require to declare global variable single time outside the modules.

It is ideally used for storing “constants” as it helps you keep the consistency.

A Global variable is useful when multiple functions are accessing the same data.

Advantages of using Local Variables

The use of local variables offer a guarantee that the values of variables will remain intact while the task is running

If several tasks change a single variable that is running simultaneously, then the result may be unpredictable. But declaring it as local variable solves this issue as each task will create its own instance of the local variable.

You can give local variables the same name in different functions because they are only recognized by the function they are declared in.

Local variables are deleted as soon as any function is over and release the memory space which it occupies.

Too many variables declared as global, then they remain in the memory till program execution is completed. This can cause of Out of Memory issue.

Data can be modified by any function. Any statement written in the program can change the value of the global variable. This may give unpredictable results in multi-tasking environments.

If global variables are discontinued due to code refactoring, you will need to change all the modules where they are called.

The debugging process of a local variable is quite tricky.

Common data required to pass repeatedly as data sharing is not possible between modules.

They have a very limited scope.

What is more useful?

What Is Generative Ai?

Generative AI is the use of artificial intelligence (AI) systems to generate original media such as text, images, video, or audio in response to prompts from users. Popular generative AI applications include ChatGPT, Bard, DALL-E, and Midjourney.

Most generative AI is powered by deep learning technologies such as large language models (LLMs). These are models trained on a vast quantity of data (e.g., text) to recognize patterns so that they can produce appropriate responses to the user’s prompts.

This technology has seen rapid growth in sophistication and popularity in recent years, especially since the release of ChatGPT in November 2023. The ability to generate content on demand has major implications in a wide variety of contexts, such as academia and creative industries.

How does generative AI work?

Generative AI is a broad concept that can theoretically be approached using a variety of different technologies. In recent years, though, the focus has been on the use of neural networks, computer systems that are designed to imitate the structures of brains.

Highly complex neural networks are the basis for large language models (LLMs), which are trained to recognize patterns in a huge quantity of text (billions or trillions of words) and then reproduce them in response to prompts (text typed in by the user).

An LLM generates each word of its response by looking at all the text that came before it and predicting a word that is relatively likely to come next based on patterns it recognizes from its training data. You can think of it as a supercharged version of predictive text. The fact that it generally works so well seems to be a product of the enormous amount of data it was trained on.

LLMs, especially a specific type of LLM called a generative pre-trained transformer (GPT), are used in most current generative AI applications—including many that generate something other than text (e.g., image generators like DALL-E). This means that things like images, music, and code can be generated based only on a text description of what the user wants.

Types of generative AI

Generative AI has a variety of different use cases and powers several popular applications. The table below indicates the main types of generative AI application and provides examples of each.

Strengths and limitations of generative AI

Generative AI is a powerful and rapidly developing field of technology, but it’s still a work in progress. It’s important to understand what it excels at and what it tends to struggle with so far.

Strengths

Generative AI technology is often flexible and can generalize to a variety of tasks rather than specializing in just one. This opens up opportunities to explore its use in a wide range of contexts.

This technology can make any business processes that involve generating text or other content (e.g., writing emails, planning projects, creating images) dramatically more efficient, allowing small teams to accomplish more and bigger teams to focus on more ambitious projects.

Generative AI tools allow non-experts to approach tasks they would normally be unable to handle. This allows people to explore areas of creativity and work that were previously inaccessible to them.

Limitations

Generative AI models often hallucinate—for example, a chatbot’s answers might be factually incorrect, or an image generator’s outputs might contain incongruous details like too many fingers on a person’s hand. Outputs should always be checked for accuracy and quality.

These tools are trained on datasets that may be biased in various ways (e.g., sexism), and the tools can therefore reproduce those biases. For example, an image generator asked to provide an image of a CEO may be more likely to show a man than a woman.

Although they’re trained on large datasets and draw on all that data for their responses, generative AI tools generally can’t tell you what sources they’re using in a specific response. This means it can be difficult to trace the sources of, for example, factual claims or visual elements.

Implications of generative AI

The rise of generative AI raises a lot of questions about the effects—positive or negative—that different applications of this technology could have on a societal level. Commonly discussed issues include:

Jobs and automation: Many people are concerned about the effects of generative AI on various creative jobs. For example, will it be harder for illustrators to find work when they have to compete with image generators? Others claim that these tools will force various industries to adapt but also create new roles as existing tasks are automated.

Effects on academia: Many academics are concerned about ChatGPT cheating among their students and about the lack of clear guidelines on how to approach these tools. University policies on AI writing are still developing.

Plagiarism and copyright concerns: Some argue that generative AI’s use of sources from its training data should be treated as plagiarism or copyright infringement. For example, some artists have attempted legal action against AI companies, arguing that image generators use elements of their work and stylistic approach without acknowledgement or compensation.

Fake news and scams: Generative AI tools can be used to deliberately spread misinformation (e.g., deepfake videos) or enable scams (e.g., imitating someone’s voice to steal their identity). They can also spread misinformation by accident if people assume, for example, that everything ChatGPT claims is factually correct without checking it against a credible source.

Future developments: There is a lot of uncertainty about how AI is likely to develop in the future. Some argue that the rapid developments in generative AI are a major step towards artificial general intelligence (AGI), while others suspect that we’re reaching the limits of what can be done with current approaches to AI and that future innovations will use very different techniques.

Other interesting articles

If you want to know more about ChatGPT, AI tools, fallacies, and research bias, make sure to check out some of our other articles with explanations and examples.

Frequently asked questions about generative AI Cite this Scribbr article

Caulfield, J. Retrieved July 19, 2023,

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