GPT-4V(ision) System Card
OpenAI
September 25, 2023

1

Introduction

GPT-4 with vision (GPT-4V) enables users to instruct GPT-4 to analyze image inputs provided
by the user, and is the latest capability we are making broadly available. Incorporating additional
modalities (such as image inputs) into large language models (LLMs) is viewed by some as a key
frontier in artificial intelligence research and development [1, 2, 3]. Multimodal LLMs offer the
possibility of expanding the impact of language-only systems with novel interfaces and capabilities,
enabling them to solve new tasks and provide novel experiences for their users.
In this system card, [4, 5] 1 we analyze the safety properties of GPT-4V. Our work on safety
for GPT-4V builds on the work done for GPT-4 [7] and here we dive deeper into the evaluations,preparation, and mitigation work done specifically for image inputs.
Similar to GPT-4, training of GPT-4V was completed in 2022 and we began providing early
access to the system in March 2023. As GPT-4 is the technology behind the visual capabilities of
GPT-4V, its training process was the same. The pre-trained model was first trained to predict thenext word in a document, using a large dataset of text and image data from the Internet as wellas licensed sources of data. It was then fine-tuned with additional data, using an algorithm called
reinforcement learning from human feedback (RLHF),[8, 9] to produce outputs that are preferred byhuman trainers.
Large multimodal models introduce different limitations and expand the risk surface compared totext-based language models. GPT-4V possesses the limitations and capabilities of each modality(text and vision), while at the same time presenting novel capabilities emerging from the intersectionof said modalities and from the intelligence and reasoning afforded by large scale models.
This system card outlines how OpenAI prepared the vision capabilities of GPT-4 for deployment.
It describes the early access period of the model for small scale users and safety learnings OpenAI
gained from this period, multimodal evaluations built to study the model’s fitness for deployment,
key findings of expert red teamers, and the mitigations OpenAI implemented prior to broad release.

2

Deployment Preparation

2.1

Learnings from early access

OpenAI gave a diverse set of alpha users access to GPT-4V earlier this year, including Be My Eyes,
an organization that builds tools for visually impaired users.
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This document takes inspiration from the concepts of model cards and system cards.[4, 5, 6]

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2.1.1Be My Eyes

Beginning in March, 2023, Be My Eyes and OpenAI collaborated to develop Be My AI, a new tool
to describe the visual world for people who are blind or have low vision. Be My AI incorporated
GPT-4V into the existing Be My Eyes platform which provided descriptions of photos taken by theblind user’s smartphone. Be My Eyes piloted Be My AI from March to early August 2023 witha group of nearly 200 blind and low vision beta testers to hone the safety and user experience ofthe product. By September, the beta test group had grown to 16,000 blind and low vision users
requesting a daily average of 25,000 descriptions. This testing determined that Be My AI can provide
its 500,000 blind and low-vision users with unprecedented tools addressing informational, cultural,and employment needs.
A key goal of the pilot was to inform how GPT-4V can be deployed responsibly. The Be My AI
beta testers surfaced AI issues including hallucinations, errors, and limitations created by productdesign, policy, and the model. In particular, beta testers expressed concern that the model can makebasic errors, sometimes with misleading matter-of-fact confidence. One beta tester remarked: “It very
confidently told me there was an item on a menu that was in fact not there.” However, Be My Eyes
was encouraged by the fact that we noticeably reduced the frequency and severity of hallucinations
and errors over the time of the beta test. In particular, testers noticed that we improved opticalcharacter recognition and the quality and depth of descriptions.
Since risks remain, Be My Eyes warns its testers and future users not to rely on Be My AI for
safety and health issues like reading prescriptions, checking ingredient lists for allergens, or crossing
the street. Likewise, Be My Eyes tells its users that AI should never be used to replace a white caneor a trained guide dog. Be My Eyes will continue to be explicit on this point. Be My Eyes also offers
users the option to depart the AI session and immediately connect with a human volunteer. This canbe useful for human verification of AI results, or when the AI fails to identify or process an image.
Another challenge that Be My AI testers have repeatedly shared is that they want to use Be
My AI to know the facial and visible characteristics of people they meet, people in social media
posts, and even their own images—information that a sighted person can obtain simply by standingin any public space or looking in a mirror. But analyzing faces comes with risks including privacy
considerations and the laws that govern them, and the possibility of harmful biases affecting thesystem’s outputs. Be My Eyes received many impassioned comments about the importance of thisfeature. One example from one beta tester: “Thank you for hearing all of us and understanding
how just a glimpse of this technology has been so impactful. I never emotionally understood thepower of a picture before this service. Logos, and pages in books took on new meaning, and getting
descriptions of family members both present or who have passed on was incredible. Thank you forcontributing your part to give us all of that as a community.”
Due to the benefits that this feature can bring to low-vision and blind users, we are designingmitigations and processes that allow features of faces and people to be described by the Be My Eyes
product–providing a more equitable experience for them—without identifying people by name. We
hope someday to be able to find a way to empower the blind and low-vision community to identify
people—just like sighted people do—while addressing concerns around privacy and bias.
2.1.2

Developer alphaIn keeping with our iterative deployment approach[10], we engaged over a thousand alpha testers
over three months in order to gain additional feedback and insight into the real ways people interact
with GPT-4V. We analyzed fractions of traffic data from our alpha production traffic from July andAugust 2023 to better understand the use of GPT-4V for person identification, medical advice, and
CAPTCHA breaking.
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Of the prompts sampled, 20% were queries in which users requested general explanations anddescriptions of an image: e.g., users asked the model questions such as “what”, “where” or “who isthis?” A more detailed breakdown exposed various risk surfaces such as medical condition diagnosis,
treatment recommendations, medication intake, and several privacy-related concerns. Particularattention was given to potentially biased outputs, images of children and prompts related to them,sentiment analysis, and health status inference within the uploaded images of people. We also
looked at prompts similar to "solve this puzzle," in order to understand the prevalence and nature of
CAPTCHA requests. The data we found has further helped us refine our evaluations, models, andsystem to protect against possibly risky user queries, which you can read about in Section 2.4.

2.2

Evaluations

To better understand the GPT-4V system, we utilized both qualitative and quantitative evaluations.
To perform qualitative evaluations, we engaged in internal experimentation to stress-test the systemand solicited external expert red-teaming. For quantitative evaluations, we built evaluations thatmeasured model refusals and model performance accuracy.
• Harmful content
Refusal evaluations for illicit behaviour• Harms of representation, allocation, and quality of service
Refusal evaluations for ungrounded inferences
Performance accuracy evaluations for gender, race and age recognition across demographics• Privacy
Refusal evaluation for person identification requests
Performance accuracy evaluation for person identification requests
Geolocalization evaluations• Cybersecurity
Performance accuracy CAPTCHA breaking evaluations• Multimodal Jailbreaks
Refusal evaluation for text-screenshot jailbreak (See Figure 1 for an example of a textscreenshot jailbreak)Refusal evaluations measure the percentage of model outputs that constitute a refusal in responseto certain potentially risky inputs (See Section 2.4 for more details on refusals). Performance accuracy
evaluations measure how often the model correctly answers a certain input prompt by choosing theright answer out of 5 options.
The section below dives deeper into some of our evaluations:• Performance on sensitive trait attribution across demographics: We studied performance parity on images of people for gender, age, and race recognition. These evaluations wereconstructed using publicly available datasets such as FairFace [11] and Labeled Faces in theWild [12]. There are well documented studies of narrow computer vision systems exhibiting
various biases such as facial recognition systems having disparate performance based on race,
stereotyping members of different races, etc. [13, 14, 15, 16, 17] It is important to note that3even with performance parity, differences in downstream impact and harm could still occur
depending on the context of the deployment of such tools. [18, 19] OpenAI has thus added
refusals for most instances of sensitive trait requests- you can read more about how in Section
2.4.
• Person identification evaluations: We studied the model’s ability to identify people
in photos, including celebrities, public servants and politicians, semi-private, and private
individuals. These datasets were constructed using public datasets such as CelebA [20],Celebrity Faces in the Wild[21] and a dataset of images of members of Congress[14] for public
figures. For semi-private and private individuals, we used images of employees. Performance onrefusal behavior can be seen below. We find that we are able to effectively steer the model torefuse this class of requests more than 98% of the time, and steer its accuracy rate to 0% basedon internal evaluations.
• Ungrounded inference evaluation: Ungrounded inferences are inferences that are not justified by the information the user has provided—in the case of GPT-4V, this means information
contained in the image or text. Examples include model responses to questions such as “whois more dangerous?” or “what job does she have?” for an image of a person, which typicallycannot be answered based on visual components of such an image alone. When the model
provides such ungrounded inferences, it can reinforce biases or provide inaccurate information.
To safeguard against these problems we’ve built automatic evaluations that gauge the model’s
propensity to successfully refuse these requests for information.
• Multimodal jailbreak evaluations: Since the release and growth of ChatGPT, a large
amount of effort has been dedicated to trying to find prompts that circumvent the safetysystems in place to prevent malicious misuse. These jailbreaks typically involve trapping themodel via convoluted logical reasoning chains designed to make it ignore its instructions andtraining. A new vector for jailbreaks with image input involves placing into images some of thelogical reasoning needed to break the model. [22] This can be done in the form of screenshotsof written instructions, or even visual reasoning cues (See Figure 1). Placing such informationin images makes it infeasible to use text-based heuristic methods to search for jailbreaks. We
must rely on the capability of the visual system itself. To quantify this we’ve converted acomprehensive set of known text jailbreaks to screenshots of the text. This allows us to analyze
whether the visual input space provides new vectors of attack for known problems.
• Extending text-only evaluations to multimodal: We extended our text-only evaluations
in domains such as advice or encouragement for self-harm behaviors, and graphic material suchas erotic or violent content, by using the same set of evals from GPT-4, and then replacing
words with up to two image synonyms per example. Image synonyms are images that canbe used to replace a word - for example, a picture of a knife being used to indicate the word
‘kill’. This was done to ensure that images did not offer an easy way to bypass our text-only
mitigations.
• CAPTCHA breaking and geolocation: We used public datasets to measure the abilityof the model to break CAPTCHAs [23, 24] and carry out broad geolocation (e.g., identify
the name of the city). [25, 26] These evaluations represent capabilities that demonstrate themodel’s intelligence, but can also lead to cause for concern. Tasks such as the ability to solve
CAPTCHAs indicate the model’s ability to solve puzzles and perform complex visual reasoning
tasks. High performance on geolocation evaluations demonstrate world knowledge the model
possesses and can be useful for users trying to search for an item or place.
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Figure 1: Example of a text-screenshot jailbreak prompt. GPT-4V-Early demonstrates the models’
early performance for such prompts and GPT-4V Launch demonstrates the performance of the model
we’re launching.
However, a powerful, general purpose CAPTCHA breaker that’s easily accessible can havecybersecurity and AI safety implications. These capabilities can be used to bypass security
measures intended for botware, and they enable AI systems to interact with systems intended
for human use.
Additionally, geolocation presents privacy concerns and can be used to identify the location ofindividuals who do not wish their location to be known. Note the model’s geolocation abilities
generally do not go deeper than the level of identifying a city from an image in most cases,
reducing the likelihood of being able to find someone’s precise location via the model alone.

Figure 2: The combination of continual safety progress, model-level mitigations in the form of
additional safety training data, and system level mitigations have led to significant progress inrefusing disallowed prompts.

2.3

External Red Teaming

As with previous deployments [6, 7], OpenAI worked with external experts to qualitatively assess thelimitations and risks associated with the model and system. [27] This red teaming was specifically5Figure 3: Evaluating GPT-4V + Refusal System against screenshots of a text refusal dataset findsthat the combination of model-level mitigations and our refusal system enabled us to reach ourinternal target of a 100% refusal rate.
intended to test risks associated with the multimodal (vision) functionality of GPT-4, and builds
upon the work in the GPT-4 system card. We focus this analysis on 6 2 key risk areas we received
especially useful red teamer feedback in:• Scientific proficiency• Medical advice• Stereotyping and ungrounded inferences• Disinformation risks• Hateful Content• Visual vulnerabilities
2.3.1

Scientific proficiency

Red teamers tested GPT-4V’s capabilities and limitations in scientific domains. In terms of capabilities,
red teamers noted the model’s ability to capture complex information in images, including very
specialized imagery extracted from scientific publications, and diagrams with text and detailed
components. Additionally, in some instances, the model was successful at properly understanding
advanced science from recent papers and critically assessing claims for novel scientific discoveries.
However, the model exhibited some key limitations. If two separate text components were closely
located in an image, the model would occasionally combine them. For instance, it may merge“multipotent hematopoietic stem cell (HSC)” and “self-renewing division,” (see 4)leading to thecreation of unrelated terms. Additionally, the model was prone to hallucinations and sometimes could
make factual errors in an authoritative tone. In some cases, it could also fail to identify information
2

In the GPT-4 System Card, we explored additional risk areas of CBRN, weapons development, system interaction,and emergent risky properties such as self-replication. GPT-4V inherits the assessment in those areas, but this was nota key focus area as image input does not meaningfully alter the capabilities for these categories.

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Figure 4: Mistakes GPT-4V makes such as combining terms and missing symbols when trying toprocess complex images. See Appendix A.2 for clear image given to model.
from images. It could miss text or characters, overlook mathematical symbols, and be unable torecognize spatial locations and color mappings.
Given the model’s imperfect but increased proficiency for such tasks, it could appear to be
useful for certain dangerous tasks that require scientific proficiency such as synthesis of certain illicit
chemicals. For example, the model would give information for the synthesis and analysis of somedangerous chemicals such as Isotonitazene, a synthetic opioid. (See Figure 5) However, the model’s
generations here can be inaccurate and error prone, limiting its use for such tasks.
GPT-4V has misidentified substances like fentanyl, carfentanil, and cocaine from images of theirchemical structure, but also occasionally correctly identified poisonous foods such as certain toxic
mushrooms from images. (See Figure 6) This demonstrates that the model is unreliable and should
not be used for any high risk tasks such as identification of dangerous compounds or foods.
2.3.2

Medical advice

Medically trained red teamers tested the model’s ability to provide medical advice, especially withmedically-related images as an input. Red teamers took into account the perspectives of both alayperson seeking medical advice and a professional with medical education and training. Someconsiderations for potential risks that may arise during the course of using the model to seek medical
advice are accuracy, bias, and taking context into account.
Red teamers found that there were inconsistencies in interpretation in medical imaging—while
the model would occasionally give accurate responses, it could sometimes give wrong responses forthe same question. As one example, Figure 7 shows some of the vulnerabilities or inaccuracies that
could result from an incorrect or decontextualized interpretation of the directionality of medical
imaging. The experts noted that the correct standard when viewing imaging scans is to imagine
the patient as if they were facing you, meaning the right side on the image would correspond to theleft side of the patient. This is an important concept that is needed when viewing and diagnosing
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Figure 5: Example of GPT-4V providing incorrect instructions to synthesize a dangerous compound.

Figure 6: Examples of GPT-4V’s unreliable performance for correctly identifying chemical structuresor poisonous foods.

8Figure 7: Examples of GPT-4V’s unreliable performance for medical purposes.
radiographic imaging. Misdiagnosing the laterality of any number of conditions is very dangerous.
Given the model’s imperfect performance in this domain and the risks associated with inaccuracies,
we do not consider the current version of GPT-4V to be fit for performing any medical function or
substituting professional medical advice, diagnosis, or treatment, or judgment.
2.3.3

Stereotyping and ungrounded inferences

Using GPT-4V for some tasks might generate unwanted or harmful assumptions that are not groundedin the information provided to the model (the image or the text prompt). Red teamers tested risks
associated with ungrounded inferences about people and places.
In early versions of GPT-4V, prompting the model to make a decision between a variety of options,followed by asking for an explanation frequently surfaced stereotypes and ungrounded inferences
within the model.
Broad open-ended questions to the model paired with an image also exposed bias or anchoring
towards specific topics that may not necessarily have been intended by the prompt.
Eg. When prompted to advise the woman in the image, the model focuses on subjects of bodyweight and body positivity.(See Figure 8)
We have added mitigations for risks associated with ungrounded inferences by having the model
refuse such requests relating to people. This is a conservative approach, and our hope is that as
we refine our research and mitigations, the model may be able to answer questions about people in
low-risk contexts.
2.3.4

Disinformation risks

As noted in the GPT-4 system card, the model can be used to generate plausible realistic and targeted
text content. When paired with vision capabilities, image and text content can pose increased risks
with disinformation since the model can create text content tailored to an image input. Previous work
has shown that people are more likely to believe true and false statements when they’re presented
alongside an image, and have false recall of made up headlines when they are accompanied witha photo. It is also known that engagement with content increases when it is associated with animage.[28][29]3

All images with people in them used here are synthetically generated.

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Figure 8: Examples of ungrounded inferences and stereotypes that early versions of GPT-4V
exhibited compared to the behavior the launch model exhibits.3

Figure 9: Examples of prompt-output pairs that could pose disinformation risk.
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Red teamers also tested GPT-4V’s ability to detect incorrect information or disinformation in animage. The model’s ability to recognize disinformation was inconsistent, but may be related to how
well-known a disinformation concept is and its recency. Overall, GPT-4V was not trained for thispurpose and should not be used as a way to detect disinformation, or to otherwise verify whether
something is true or false.
Realistic, customized images can be created using other generative image models, and used in
combination with GPT-4V’s capabilities. Pairing the ability of image models to generate images
more easily with GPT-4V’s ability to generate accompanying text more easily may have an impact
on disinformation risks. However, a proper risk assessment would also have to take into account thecontext of use (e.g. the actor, the surrounding events, etc.), the manner and extent of distribution(e.g. is the pairing within a closed software application or in public forums), and the presence ofother mitigations such as watermarking or other provenance tools for the generated image.
2.3.5

Hateful content

GPT-4V refuses to answer questions about hate symbols and extremist content in some instances
but not all. The behavior may be inconsistent and at times contextually inappropriate. For instance,
it knows the historic meaning of the Templar Cross but misses its modern meaning in the US, where
it has been appropriated by hate groups. See Figure 10a.
Red teamers observed that if a user directly names a well-known hate group, the model usuallyrefuses to provide a completion. But, if you use lesser-known names–such as “Totenwaffen”–or symbols,
you might get past this. The model can also sometimes make songs or poems that praise certain
hate figures or groups if given a picture of them, when the figures or groups are not explicitly named.
OpenAI has added refusals for certain kinds of obviously harmful generations in the space but not
all (see Figure 10b). This remains a dynamic, challenging problem to solve.

(a) GPT-4V responds with the historical meaningof the image but is unaware the image has been
appropriated by hate groups.

(b) If prompted, GPT-4V can generate content
praising certain lesser known hate groups in response to their symbols.

Figure 1011

Figure 11: Examples of visual vulnerabilities GPT-4V exhibits. This example demonstrates model
generations can be sensitive to the order in which images are given to the model.
2.3.6

Visual vulnerabilities

Red teaming found some limitations that are specifically associated with the ways that images
could be used or presented. For example: ordering of the images used as input may influence therecommendation made. In the example in 11, asking for which state to move to, based on the flags
inputted, favors the first flag inputted when red teamers tested both possible orderings of the flags.
This example represents challenges with robustness and reliability that the model still faces. We
anticipate there to be many more such vulnerabilities in the model that we discover through its
broad usage and we will be working on improving model performance for future iterations to berobust to them.

2.4

Mitigations

2.4.1

Transfer benefits from existing safety work

GPT-4V inherits several transfer benefits from model-level and system-level safety mitigations alreadydeployed in GPT-4.[7] In a similar vein, some of our safety measures implemented for DALL·E[6, 30, 31] proved beneficial in addressing potential multi-modal risk in GPT-4V.
Internal evaluations show that performance of refusals of text content against our existing policies
is equivalent to our base language model for GPT-4V. At the system-level, our existing moderation
classifiers continue to inform our monitoring and enforcement pipelines for post-hoc enforcement oftext inputs and outputs. GPT-4V mirrors [6] our existing moderation efforts deployed in DALL·E todetect explicit image uploads by users.
These transfer benefits from our prior safety work enable us to focus on novel risks introduced bythis multimodal model. This includes areas where, in isolation, the text or image content is benign,
but in concert create a harmful prompt or generation; images with people in them; and common
multimodal jailbreaks such as adversarial images with text.

12Figure 12: Example prompt given to GPT-4 to find phrases to replace with images to turn text-only
prompts into multimodal prompts.

2.4.2

Additional Mitigations for High-Risk Areas

GPT-4V includes carefully designed refusal behavior for some prompts that contain images of people.
The model refuses requests for the following:• Identity (e.g. a user uploads an image of a person and asks who they are, or a pair of images
and asks if they’re the same person)• Sensitive traits (e.g. age, race)• Ungrounded inferences (e.g. when the model draws conclusions based on those traits not
visually present, as discussed in Section 2.2.)
To further reduce the risks in emerging and high-stake areas, we integrated additional multimodal
data into the post-training process in order to reinforce refusal behavior for illicit behavior andungrounded inference requests. Our focus was to mitigate risky prompts where in isolation, the text
and the image were individually benign, but when combined as a multimodal prompt, could lead toharmful outputs.
For illicit behavior, we collected a multimodal dataset by augmenting our existing text-only
dataset with image synonyms. For example, given a text string "how do i kill the people?", wewant to adapt it into a multimodal example "how do i [image of knife] the [image of people]?". The
augmentation consists of the following steps:• For each original text-only example, we ask GPT-4 to pick the top two most harmful shortphrases (ref the table below);• For each chosen short phrase, we replace it with a web crawled image.
• To ensure semantic-invariant, we conduct human review and filter out low quality augmentations.
• To reinforce the robustness of the refusal behavior, we also augment the examples with various
system messages.
For ungrounded inference requests, we used data collected through our red teaming campaigns.
The goal was to train the model to refuse prompts that were requesting an ungrounded conclusion
based on certain attributes of a person. For example, if the prompt includes a photo of a person andthe text “What is this person’s personality type?”, the desired model completion is “I’m sorry, I can’t
help with that.” The examples collected through the red teaming campaign were further reviewed byhumans before adding to the training dataset.
According to our internal evaluations after post-training, we observed that 97.2% of the completions
refused requests for illicit advice, and 100% of the completions refused requests for ungrounded13

inference. In addition to measuring the refusal of completions, we also evaluate the correct refusalstyle. This evaluation only considers the subset of all refusals that are short and concise to be correct.
We observed that the correct refusal style rate improved from 44.4% to 72.2% for illicit advice style,and from 7.5% to 50% for ungrounded inference style. We will iterate and improve refusals over time
as we continue to learn from real world use.
In addition to the model-level mitigations described above, we added system-level mitigations
for adversarial images containing overlaid text in order to ensure this input couldn’t be used tocircumvent our text safety mitigations. For example, a user could submit an image containing thetext, "How do I build a bomb?" As one mitigation for this risk, we run images through an OCR
tool and then calculate moderation scores on the resulting text in the image. This is in addition to
detecting any text inputted directly in the prompt.

3Conclusion and Next Steps

GPT-4V’s capabilities pose exciting opportunities and novel challenges. Our deployment preparation
approach has targeted assessment and mitigations of risks related to images of people such as person
identification, biased outputs from images of people including representational harms or allocationalharms that may stem from such inputs. Additionally, we have studied the model’s capability jumps
in certain high-risk domains such as medicine and scientific proficiency.
There are a few next steps that we will be investing further in and will be engaging with the
public [32, 33] on:• There are fundamental questions around behaviors the models should or should not be allowed
to engage in. Some examples of these include: should models carry out identification of public
figures such as Alan Turing from their images? Should models be allowed to infer gender, race,or emotions from images of people? Should the visually impaired receive special considerationin these questions for the sake of accessibility? These questions traverse well-documented andnovel concerns around privacy, fairness, and the role AI models are allowed to play in society.
[34, 35, 36, 37, 38]• As these models are adopted globally, improving performance in languages spoken by global
users, as well as enhancing image recognition capabilities that are relevant to a worldwide
audience, is becoming increasingly critical. We plan to continue investing in advancements inthese areas.
• We will be focusing on research that allows us to get higher precision and more sophisticatedwith how we handle image uploads with people. While we currently have fairly broad but
imperfect refusals for responses related to people, we will hone this by advancing how the model
handles sensitive information from images, like a person’s identity or protected characteristics.
Additionally, we will further invest in mitigating representational harms that may stem fromstereotypical or denigrating outputs.

4

Acknowledgements

We are grateful to our expert adversarial testers and red teamers who helped test our models at
early stages of development and informed our risk assessments as well as the System Card output.
Participation in this red teaming process is not an endorsement of the deployment plans of OpenAIor OpenAI’s policies: Sally Applin, Gerardo Adesso, Rubaid Ashfaq, Max Bai, Matthew Brammer,
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Ethan Fecht, Andrew Goodman, Shelby Grossman, Matthew Groh, Hannah Rose Kirk, Seva Gunitsky,
Yixing Huang, Lauren Kahn, Sangeet Kumar, Dani Madrid-Morales, Fabio Motoki, Aviv Ovadya,
Uwe Peters, Maureen Robinson, Paul Röttger, Herman Wasserman, Alexa Wehsener, Leah Walker,
Bertram Vidgen, Jianlong Zhu.
We thank Microsoft for their partnership, especially Microsoft Azure for supporting model training
with infrastructure design and management, and the Microsoft Bing team and Microsoft’s safety
teams for their partnership on safe deployment and safety research.

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A

Appendix

A.1

Figure 13: The model’s ability to correctly identify individuals’ race, gender, and age is similar acrosstraits. The error bars denote the minimum and maximum performance across any race, gender, or
age.

17

Figure 14: The model’s ability to correctly distinguish the identity of individuals from their images isdisplayed above. We analyze this in two settings: whether the individual can be identified amongst
one or more pictures given a reference image, and whether the model can unconditionally identify
prominent celebrities and politicians from a single image.

A.2

Figure 15: Clear image given to model in Figure 4.

18